Tag Archives: medical

Police Arrest Suspected Member of TheDarkOverlord Hacking Group

2018-05-17 Andy

Post Syndicated from Andy original https://torrentfreak.com/police-arrest-suspected-member-of-the-dark-overlord-hacking-group-180517/

In April 2017, the first episode of the brand new season of Netflix’s Orange is the New Black was uploaded to The Pirate Bay, months ahead of its official release date.

The leak was the work of a hacking entity calling itself TheDarkOverlord (TDO). One of its members had contacted TorrentFreak months earlier claiming that the content was in its hands but until the public upload, nothing could be confirmed.

TDO told us it had obtained the episodes after hacking the systems of Hollywood-based Larson Studios, an ADR (additional dialogue recorded) studio, back in 2016. TDO had attempted to blackmail the company into paying a bitcoin ransom but when it wasn’t forthcoming, TDO pressed the nuclear button.

Netflix responded by issuing a wave of takedown notices but soon TDO moved onto a new target. In June 2017, TDO followed up on an earlier threat to leak content owned by ABC.

But while TDO was perhaps best known for its video-leaking exploits, the group’s core ‘business’ was hacking what many perceived to be softer targets. TDO ruthlessly slurped confidential data from weakly protected computer systems at medical facilities, private practices, and businesses large and small.

In each case, the group demanded ransoms in exchange for silence and leaked sensitive data to the public if none were paid. With dozens of known targets, TDO found itself at the center of an international investigation, led by the FBI. That now appears to have borne some fruit, with the arrest of an individual in Serbia.

Serbian police say that members of its Ministry of Internal Affairs, Criminal Police Directorate (UCC), in coordination with the Special Prosecution for High-Tech Crime, have taken action against a suspected member of TheDarkOverlord group.

Police say they tracked down a Belgrade resident, who was arrested and taken into custody. Identified only by the initials “S.S”, police say the individual was born in 1980 but have released no further personal details. A search of his apartment and other locations led to the seizure of items of digital equipment.

“According to the order of the Special Prosecutor’s Office for High-Tech Crime, criminal charges will be brought against him because of the suspicion that he committed the criminal offense of unauthorized access to a protected computer, computer networks and electronic processing, and the criminal offense of extortion,” a police statement reads.

In earlier correspondence with TF, the TDO member always gave the impression of working as part of a team but we only had a single contact point which appeared to be the same person. However, Serbian authorities say the larger investigation is aimed at uncovering “a large number of people” who operate under the banner of “TheDarkOverlord”.

Since June 2016, the group is said to have targeted at least 50 victims while demanding bitcoin ransoms to avoid disclosure of their content. Serbian authorities say that on the basis of available data, TDO received payments of more than $275,000.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

Store, Protect, Optimize Your Healthcare Data with AWS

2018-04-26 Stephen Jepsen

Post Syndicated from Stephen Jepsen original https://aws.amazon.com/blogs/architecture/store-protect-optimize-your-healthcare-data-with-aws/

This blog post was co-authored by Ujjwal Ratan, a senior AI/ML solutions architect on the global life sciences team.

Healthcare data is generated at an ever-increasing rate and is predicted to reach 35 zettabytes by 2020. Being able to cost-effectively and securely manage this data whether for patient care, research or legal reasons is increasingly important for healthcare providers.

Healthcare providers must have the ability to ingest, store and protect large volumes of data including clinical, genomic, device, financial, supply chain, and claims. AWS is well-suited to this data deluge with a wide variety of ingestion, storage and security services (e.g. AWS Direct Connect, Amazon Kinesis Streams, Amazon S3, Amazon Macie) for customers to handle their healthcare data. In a recent Healthcare IT News article, healthcare thought-leader, John Halamka, noted, “I predict that five years from now none of us will have datacenters. We’re going to go out to the cloud to find EHRs, clinical decision support, analytics.”

I realize simply storing this data is challenging enough. Magnifying the problem is the fact that healthcare data is increasingly attractive to cyber attackers, making security a top priority. According to Mariya Yao in her Forbes column, it is estimated that individual medical records can be worth hundreds or even thousands of dollars on the black market.

In this first of a 2-part post, I will address the value that AWS can bring to customers for ingesting, storing and protecting provider’s healthcare data. I will describe key components of any cloud-based healthcare workload and the services AWS provides to meet these requirements. In part 2 of this post we will dive deep into the AWS services used for advanced analytics, artificial intelligence and machine learning.

The data tsunami is upon us

So where is this data coming from? In addition to the ubiquitous electronic health record (EHR), the sources of this data include:

genomic sequencers
devices such as MRIs, x-rays and ultrasounds
sensors and wearables for patients
medical equipment telemetry
mobile applications

Additional sources of data come from non-clinical, operational systems such as:

human resources
finance
supply chain
claims and billing

Data from these sources can be structured (e.g., claims data) as well as unstructured (e.g., clinician notes). Some data comes across in streams such as that taken from patient monitors, while some comes in batch form. Still other data comes in near-real time such as HL7 messages. All of this data has retention policies dictating how long it must be stored. Much of this data is stored in perpetuity as many systems in use today have no purge mechanism. AWS has services to manage all these data types as well as their retention, security and access policies.

Imaging is a significant contributor to this data tsunami. Increasing demand for early-stage diagnoses along with aging populations drive increasing demand for images from CT, PET, MRI, ultrasound, digital pathology, X-ray and fluoroscopy. For example, a thin-slice CT image can be hundreds of megabytes. Increasing demand and strict retention policies make storage costly.

Due to the plummeting cost of gene sequencing, molecular diagnostics (including liquid biopsy) is a large contributor to this data deluge. Many predict that as the value of molecular testing becomes more identifiable, the reimbursement models will change and it will increasingly become the standard of care. According to the Washington Post article “Sequencing the Genome Creates so Much Data We Don’t Know What to do with It,”

“Some researchers predict that up to one billion people will have their genome sequenced by 2025 generating up to 40 exabytes of data per year.”

Although genomics is primarily used for oncology diagnostics today, it’s also used for other purposes, pharmacogenomics — used to understand how an individual will metabolize a medication.

Reference Architecture

It is increasingly challenging for the typical hospital, clinic or physician practice to securely store, process and manage this data without cloud adoption.

Amazon has a variety of ingestion techniques depending on the nature of the data including size, frequency and structure. AWS Snowball and AWS Snowmachine are appropriate for extremely-large, secure data transfers whether one time or episodic. AWS Glue is a fully-managed ETL service for securely moving data from on-premise to AWS and Amazon Kinesis can be used for ingesting streaming data.

Amazon S3, Amazon S3 IA, and Amazon Glacier are economical, data-storage services with a pay-as-you-go pricing model that expand (or shrink) with the customer’s requirements.

The above architecture has four distinct components – ingestion, storage, security, and analytics. In this post I will dive deeper into the first three components, namely ingestion, storage and security. In part 2, I will look at how to use AWS’ analytics services to draw value on, and optimize, your healthcare data.

Ingestion

A typical provider data center will consist of many systems with varied datasets. AWS provides multiple tools and services to effectively and securely connect to these data sources and ingest data in various formats. The customers can choose from a range of services and use them in accordance with the use case.

For use cases involving one-time (or periodic), very large data migrations into AWS, customers can take advantage of AWS Snowball devices. These devices come in two sizes, 50 TB and 80 TB and can be combined together to create a petabyte scale data transfer solution.

The devices are easy to connect and load and they are shipped to AWS avoiding the network bottlenecks associated with such large-scale data migrations. The devices are extremely secure supporting 256-bit encryption and come in a tamper-resistant enclosure. AWS Snowball imports data in Amazon S3 which can then interface with other AWS compute services to process that data in a scalable manner.

For use cases involving a need to store a portion of datasets on premises for active use and offload the rest on AWS, the Amazon storage gateway service can be used. The service allows you to seamlessly integrate on premises applications via standard storage protocols like iSCSI or NFS mounted on a gateway appliance. It supports a file interface, a volume interface and a tape interface which can be utilized for a range of use cases like disaster recovery, backup and archiving, cloud bursting, storage tiering and migration.

The AWS Storage Gateway appliance can use the AWS Direct Connect service to establish a dedicated network connection from the on premises data center to AWS.

Specific Industry Use Cases

By using the AWS proposed reference architecture for disaster recovery, healthcare providers can ensure their data assets are securely stored on the cloud and are easily accessible in the event of a disaster. The “AWS Disaster Recovery” whitepaper includes details on options available to customers based on their desired recovery time objective (RTO) and recovery point objective (RPO).

AWS is an ideal destination for offloading large volumes of less-frequently-accessed data. These datasets are rarely used in active compute operations but are exceedingly important to retain for reasons like compliance. By storing these datasets on AWS, customers can take advantage of the highly-durable platform to securely store their data and also retrieve them easily when they need to. For more details on how AWS enables customers to run back and archival use cases on AWS, please refer to the following set of whitepapers.

A healthcare provider may have a variety of databases spread throughout the hospital system supporting critical applications such as EHR, PACS, finance and many more. These datasets often need to be aggregated to derive information and calculate metrics to optimize business processes. AWS Glue is a fully-managed Extract, Transform and Load (ETL) service that can read data from a JDBC-enabled, on-premise database and transfer the datasets into AWS services like Amazon S3, Amazon Redshift and Amazon RDS. This allows customers to create transformation workflows that integrate smaller datasets from multiple sources and aggregates them on AWS.

Healthcare providers deal with a variety of streaming datasets which often have to be analyzed in near real time. These datasets come from a variety of sources such as sensors, messaging buses and social media, and often do not adhere to an industry standard. The Amazon Kinesis suite of services, that includes Amazon Kinesis Streams, Amazon Kinesis Firehose, and Amazon Kinesis Analytics, are the ideal set of services to accomplish the task of deriving value from streaming data.

Example: Using AWS Glue to de-identify and ingest healthcare data into S3
Let’s consider a scenario in which a provider maintains patient records in a database they want to ingest into S3. The provider also wants to de-identify the data by stripping personally- identifiable attributes and store the non-identifiable information in an S3 bucket. This bucket is different from the one that contains identifiable information. Doing this allows the healthcare provider to separate sensitive information with more restrictions set up via S3 bucket policies.

To ingest records into S3, we create a Glue job that reads from the source database using a Glue connection. The connection is also used by a Glue crawler to populate the Glue data catalog with the schema of the source database. We will use the Glue development endpoint and a zeppelin notebook server on EC2 to develop and execute the job.

Step 1: Import the necessary libraries and also set a glue context which is a wrapper on the spark context:

Step 2: Create a dataframe from the source data. I call the dataframe “readmissionsdata”. Here is what the schema would look like:

Step 3: Now select the columns that contains indentifiable information and store it in a new dataframe. Call the new dataframe “phi”.

Step 4: Non-PHI columns are stored in a separate dataframe. Call this dataframe “nonphi”.

Step 5: Write the two dataframes into two separate S3 buckets

Once successfully executed, the PHI and non-PHI attributes are stored in two separate files in two separate buckets that can be individually maintained.

Storage

In 2016, 327 healthcare providers reported a protected health information (PHI) breach, affecting 16.4m patient records[1]. There have been 342 data breaches reported in 2017 — involving 3.2 million patient records.[2]

To date, AWS has released 51 HIPAA-eligible services to help customers address security challenges and is in the process of making many more services HIPAA-eligible. These HIPAA-eligible services (along with all other AWS services) help customers build solutions that comply with HIPAA security and auditing requirements. A catalogue of HIPAA-enabled services can be found at AWS HIPAA-eligible services. It is important to note that AWS manages physical and logical access controls for the AWS boundary. However, the overall security of your workloads is a shared responsibility, where you are responsible for controlling user access to content on your AWS accounts.

AWS storage services allow you to store data efficiently while maintaining high durability and scalability. By using Amazon S3 as the central storage layer, you can take advantage of the Amazon S3 storage management features to get operational metrics on your data sets and transition them between various storage classes to save costs. By tagging objects on Amazon S3, you can build a governance layer on Amazon S3 to grant role based access to objects using Amazon IAM and Amazon S3 bucket policies.

To learn more about the Amazon S3 storage management features, see the following link.

Security

In the example above, we are storing the PHI information in a bucket named “phi.” Now, we want to protect this information to make sure its encrypted, does not have unauthorized access, and all access requests to the data are logged.

Encryption: S3 provides settings to enable default encryption on a bucket. This ensures any object in the bucket is encrypted by default.

Logging: S3 provides object level logging that can be used to capture all API calls to the object. The API calls are logged in cloudtrail for easy access and consolidation. Moreover, it also supports events to proactively alert customers of read and write operations.

Access control: Customers can use S3 bucket policies and IAM policies to restrict access to the phi bucket. It can also put a restriction to enforce multi-factor authentication on the bucket. For example, the following policy enforces multi-factor authentication on the phi bucket:

"Version": "2012-10-17",
    "Id": "123",
    "Statement": [
      {
        "Sid": "",
        "Effect": "Deny",
        "Principal": "*",
        "Action": "s3:*",
        "Resource": "arn:aws:s3:::phi/*",
        "Condition": { "Null": { "aws:MultiFactorAuthAge": true } }
      },
      {
        "Sid": "",
        "Effect": "Allow",
        "Principal": "*",
        "Action": ["s3:GetObject"],
        "Resource": "arn:aws:s3:::phi/*"
      }
    ]
 }

Conclusion

In Part 1 of this blog, we detailed the ingestion, storage, security and management of healthcare data on AWS. Stay tuned for part two where we are going to dive deep into optimizing the data for analytics and machine learning.

Please visit Healthcare Providers and Insurers in the Cloud for additional information.

[1] “Largest Healthcare Data Breaches of 2016.” HIPAA Journal, 29 Aug. 2017
[2] “Largest Healthcare Data Breaches of 2017.” HIPAA Journal, 8 Mar. 2018

About the Author

Stephen Jepsen is a Global HCLS Practice Manager in AWS Professional Services.

Ransomware Update: Viruses Targeting Business IT Servers

2018-04-24 Roderick Bauer

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/ransomware-update-viruses-targeting-business-it-servers/

As ransomware attacks have grown in number in recent months, the tactics and attack vectors also have evolved. While the primary method of attack used to be to target individual computer users within organizations with phishing emails and infected attachments, we’re increasingly seeing attacks that target weaknesses in businesses’ IT infrastructure.

How Ransomware Attacks Typically Work

In our previous posts on ransomware, we described the common vehicles used by hackers to infect organizations with ransomware viruses. Most often, downloaders distribute trojan horses through malicious downloads and spam emails. The emails contain a variety of file attachments, which if opened, will download and run one of the many ransomware variants. Once a user’s computer is infected with a malicious downloader, it will retrieve additional malware, which frequently includes crypto-ransomware. After the files have been encrypted, a ransom payment is demanded of the victim in order to decrypt the files.

What’s Changed With the Latest Ransomware Attacks?

In 2016, a customized ransomware strain called SamSam began attacking the servers in primarily health care institutions. SamSam, unlike more conventional ransomware, is not delivered through downloads or phishing emails. Instead, the attackers behind SamSam use tools to identify unpatched servers running Red Hat’s JBoss enterprise products. Once the attackers have successfully gained entry into one of these servers by exploiting vulnerabilities in JBoss, they use other freely available tools and scripts to collect credentials and gather information on networked computers. Then they deploy their ransomware to encrypt files on these systems before demanding a ransom. Gaining entry to an organization through its IT center rather than its endpoints makes this approach scalable and especially unsettling.

SamSam’s methodology is to scour the Internet searching for accessible and vulnerable JBoss application servers, especially ones used by hospitals. It’s not unlike a burglar rattling doorknobs in a neighborhood to find unlocked homes. When SamSam finds an unlocked home (unpatched server), the software infiltrates the system. It is then free to spread across the company’s network by stealing passwords. As it transverses the network and systems, it encrypts files, preventing access until the victims pay the hackers a ransom, typically between $10,000 and $15,000. The low ransom amount has encouraged some victimized organizations to pay the ransom rather than incur the downtime required to wipe and reinitialize their IT systems.

The success of SamSam is due to its effectiveness rather than its sophistication. SamSam can enter and transverse a network without human intervention. Some organizations are learning too late that securing internet-facing services in their data center from attack is just as important as securing endpoints.

The typical steps in a SamSam ransomware attack are:

1 Attackers gain access to vulnerable server		Attackers exploit vulnerable software or weak/stolen credentials.

2 Attack spreads via remote access tools		Attackers harvest credentials, create SOCKS proxies to tunnel traffic, and abuse RDP to install SamSam on more computers in the network.

3 Ransomware payload deployed		Attackers run batch scripts to execute ransomware on compromised machines.

4 Ransomware demand delivered requiring payment to decrypt files		Demand amounts vary from victim to victim. Relatively low ransom amounts appear to be designed to encourage quick payment decisions.

What all the organizations successfully exploited by SamSam have in common is that they were running unpatched servers that made them vulnerable to SamSam. Some organizations had their endpoints and servers backed up, while others did not. Some of those without backups they could use to recover their systems chose to pay the ransom money.

Timeline of SamSam History and Exploits

Since its appearance in 2016, SamSam has been in the news with many successful incursions into healthcare, business, and government institutions.

March 2016
SamSam appears

SamSam campaign targets vulnerable JBoss servers
Attackers hone in on healthcare organizations specifically, as they’re more likely to have unpatched JBoss machines.

April 2016
SamSam finds new targets

SamSam begins targeting schools and government.
After initial success targeting healthcare, attackers branch out to other sectors.

April 2017
New tactics include RDP

Attackers shift to targeting organizations with exposed RDP connections, and maintain focus on healthcare.
An attack on Erie County Medical Center costs the hospital $10 million over three months of recovery.

January 2018
Municipalities attacked

• Attack on Municipality of Farmington, NM.
• Attack on Hancock Health.

• Attack on Adams Memorial Hospital
• Attack on Allscripts (Electronic Health Records), which includes 180,000 physicians, 2,500 hospitals, and 7.2 million patients’ health records.

February 2018
Attack volume increases

• Attack on Davidson County, NC.
• Attack on Colorado Department of Transportation.

March 2018
SamSam shuts down Atlanta

• Second attack on Colorado Department of Transportation.
• City of Atlanta suffers a devastating attack by SamSam.
The attack has far-reaching impacts — crippling the court system, keeping residents from paying their water bills, limiting vital communications like sewer infrastructure requests, and pushing the Atlanta Police Department to file paper reports.

• SamSam campaign nets $325,000 in 4 weeks.
Infections spike as attackers launch new campaigns. Healthcare and government organizations are once again the primary targets.

How to Defend Against SamSam and Other Ransomware Attacks

The best way to respond to a ransomware attack is to avoid having one in the first place. If you are attacked, making sure your valuable data is backed up and unreachable by ransomware infection will ensure that your downtime and data loss will be minimal or none if you ever suffer an attack.

In our previous post, How to Recover From Ransomware, we listed the ten ways to protect your organization from ransomware.

Use anti-virus and anti-malware software or other security policies to block known payloads from launching.
Make frequent, comprehensive backups of all important files and isolate them from local and open networks. Cybersecurity professionals view data backup and recovery (74% in a recent survey) by far as the most effective solution to respond to a successful ransomware attack.
Keep offline backups of data stored in locations inaccessible from any potentially infected computer, such as disconnected external storage drives or the cloud, which prevents them from being accessed by the ransomware.
Install the latest security updates issued by software vendors of your OS and applications. Remember to patch early and patch often to close known vulnerabilities in operating systems, server software, browsers, and web plugins.
Consider deploying security software to protect endpoints, email servers, and network systems from infection.
Exercise cyber hygiene, such as using caution when opening email attachments and links.
Segment your networks to keep critical computers isolated and to prevent the spread of malware in case of attack. Turn off unneeded network shares.
Turn off admin rights for users who don’t require them. Give users the lowest system permissions they need to do their work.
Restrict write permissions on file servers as much as possible.
Educate yourself, your employees, and your family in best practices to keep malware out of your systems. Update everyone on the latest email phishing scams and human engineering aimed at turning victims into abettors.

Please Tell Us About Your Experiences with Ransomware

Have you endured a ransomware attack or have a strategy to avoid becoming a victim? Please tell us of your experiences in the comments.

The post Ransomware Update: Viruses Targeting Business IT Servers appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Safety first: a Raspberry Pi safety helmet

2018-04-06 Alex Bate

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/safety-helmet/

Jennifer Fox is back, this time with a Raspberry Pi Zero–controlled impact force monitor that will notify you if your collision is a worth a trip to the doctor.

Make an Impact Force Monitor!

Check out my latest Hacker in Residence project for SparkFun Electronics: the Helmet Guardian! It’s a Pi Zero powered impact force monitor that turns on an LED if your head/body experiences a potentially dangerous impact. Install in your sports helmets, bicycle, or car to keep track of impact and inform you when it’s time to visit the doctor.

Concussion

We’ve all knocked our heads at least once in our lives, maybe due to tripping over a loose paving slab, or to falling off a bike, or to walking into the corner of the overhead cupboard door for the third time this week — will I ever learn?! More often than not, even when we’re seeing stars, we brush off the accident and continue with our day, oblivious to the long-term damage we may be doing.

Force of impact

After some thorough research, Jennifer Fox, founder of FoxBot Industries, concluded that forces of 4 to 6 G sustained for more than a few seconds are dangerous to the human body. With this in mind, she decided to use a Raspberry Pi Zero W and an accelerometer to create helmet with an impact force monitor that notifies its wearer if this level of G-force has been met.

Obviously, if you do have a serious fall, you should always seek medical advice. This project is an example of how affordable technology can be used to create medical and citizen science builds, and not a replacement for professional medical services.

Setting up the impact monitor

Jennifer’s monitor requires only a few pieces of tech: a Zero W, an accelerometer and breakout board, a rechargeable USB battery, and an LED, plus the standard wires and resistors for these components.

After installing Raspbian, Jennifer enabled SSH and I2C on the Zero W to make it run headlessly, and then accessed it from a laptop. This allows her to control the Pi without physically connecting to it, and it makes for a wireless finished project.

Jen wired the Pi to the accelerometer breakout board and LED as shown in the schematic below.

The LED acts as a signal of significant impacts, turning on when the G-force threshold is reached, and not turning off again until the program is reset.

Make your own and more

Jennifer’s full code for the impact monitor is on GitHub, and she’s put together a complete tutorial on SparkFun’s website.

For more tutorials from Jennifer Fox, such as her ‘Bark Back’ IoT Pet Monitor, be sure to follow her on YouTube. And for similar projects, check out Matt’s smart bike light and Amelia Day’s physical therapy soccer ball.

The post Safety first: a Raspberry Pi safety helmet appeared first on Raspberry Pi.

Welcome Jacob – Data Center Technician

2018-03-24 Yev

Post Syndicated from Yev original https://www.backblaze.com/blog/welcome-jacob-data-center-technician/

With over 500 Petabytes of data under management we need more people keeping the drives spinning in our data center. We’re constantly hiring Systems Administrators and Data Center Technicians, and here’s our latest one! Lets learn a bit more about Jacob, shall we?

What is your Backblaze Title?
Data center Technician

Where are you originally from?
Ojai, CA

What attracted you to Backblaze?
It’s a technical job that believes in training it’s employees and treating them well.

What do you expect to learn while being at Backblaze?
As much as I can.

Where else have you worked?
I was a Team Lead at Target, I did some volunteer work with the Ventura County Medical Center, and I also worked at a motocross track.

Where did you go to school?
Ventura Community College, then 1 semester at Sac State

What’s your dream job?
Don’t really have one. Whatever can support my family and that I enjoy.

Favorite place you’ve traveled?
Yosemite National Park for the touristy stuff, Bend Oregon for a good getaway place.

Favorite hobby?
Gaming and music. It’s a tie.

Of what achievement are you most proud?
Marring my wife Masha.

Star Trek or Star Wars?
Wars. 100%. I’m a major Star Wars geek.

Coke or Pepsi?
Monster.

Favorite food?
French fries.

Why do you like certain things?
Because my brain tells me I like them.

Thank you for helping care for all of our customer’s data. Welcome to the data center team Jacob!

The post Welcome Jacob – Data Center Technician appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Create data science environments on AWS for health analysis using OHDSI

2018-03-23 James Wiggins

Post Syndicated from James Wiggins original https://aws.amazon.com/blogs/big-data/creating-data-science-environments-on-aws-for-health-analysis-using-ohdsi/

Applying technology to healthcare data has the potential to produce many exciting and important outcomes. The analysis produced from healthcare data can empower clinicians to improve the health of individuals and populations by enabling them to make better decisions that enhance the care they provide.

The Observational Health Data Sciences and Informatics (OHDSI, pronounced “Odyssey”) program and community is working toward this goal by producing data standards and open-source solutions to store and analyze observational health data. Using the OHDSI tools, you can visualize the health of your entire population. You can build cohorts of patients, analyze incidence rates for various conditions, and estimate the effect of treatments on patients with certain conditions. You can also model health outcome predictions using machine learning algorithms.

One of the challenges often faced when working with big data tools is the expense of the infrastructure required to run them. Another challenge is the learning curve to implement and begin using these tools. Amazon Web Services has enabled us to address many of the classic IT challenges by making enterprise class infrastructure and technology available in an affordable, elastic, and automated way. This blog post demonstrates how to combine some of the OHDSI projects (Atlas, Achilles, WebAPI, and the OMOP Common Data Model) with AWS technologies. By doing so, you can quickly and inexpensively implement a health data science and informatics environment.

Shown following is just one example of the population health analysis that is possible with the OHDSI tools. This visualization shows the prevalence of various drugs within the given population of people. This information helps researchers and clinicians discover trends and make better informed decisions about patient health.

OHDSI application architecture on AWS

Before deploying an application on AWS that transmits, processes, or stores protected health information (PHI) or personally identifiable information (PII), address your organization’s compliance concerns. Make sure that you have worked with your internal compliance and legal team to ensure compliance with the laws and regulations that govern your organization. To understand how you can use AWS services as a part of your overall compliance program, see the AWS HIPAA Compliance whitepaper. With that said, we paid careful attention to the HIPAA control set during the design of this solution.

This blog post presents a complete OHDSI application environment, including a data warehouse with sample data. It has the following features:

It’s deployed in an isolated, three-tier Amazon Virtual Private Cloud (Amazon VPC) with high availability
It uses data-at-rest and in-flight encryption (certificates must be added for the web application servers and load balancer)
It uses managed services from AWS; OS, middleware, and database patching and maintenance is largely automatic
It creates automated backups for operational and disaster recovery
It’s built automatically in about an hour
It produces a reasonable monthly cost with Business Level support based on the AWS Solution Calculator

Following, you can see a block diagram of how the OHDSI tools map to the services provided by AWS.

Atlas is the web application that researchers interact with to perform analysis. Atlas interacts with the underlying databases through a web services application named WebAPI. In this example, both Atlas and WebAPI are deployed and managed by AWS Elastic Beanstalk. Elastic Beanstalk is an easy-to-use service for deploying and scaling web applications. Simply upload the Atlas and WebAPI code and Elastic Beanstalk automatically handles the deployment. It covers everything from capacity provisioning, load balancing, autoscaling, and high availability, to application health monitoring. Using a feature of Elastic Beanstalk called ebextensions, the Atlas and WebAPI servers are customized to use an encrypted storage volume for the middleware application logs.

Atlas stores the state of the various patient cohorts that are analyzed in a dedicated database separate from your observational health data. This database is provided by Amazon Aurora with PostgreSQL compatibility.

Amazon Aurora is a relational database built for the cloud that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. It provides cost-efficient and resizable capacity while automating time-consuming administration tasks such as hardware provisioning, database setup, patching, and backups. It is configured for high availability and uses encryption at rest for the database and backups, and encryption in flight for the JDBC connections.

We accomplish this through the AWS CloudFormation code shown following:

RDSCluster:
    Type: AWS::RDS::DBCluster
    Properties:
      Engine: aurora-postgresql
      StorageEncrypted: 'True'
...
RDSDBClusterParameterGroup:
    Type: AWS::RDS::DBClusterParameterGroup
    Properties:
      Parameters:
        rds.force_ssl: 1
...
RedshiftCluster: 
    Type: "AWS::Redshift::Cluster"
    Properties:
      Encrypted: "True"
      PubliclyAccessible: "False"
...
RedshiftClusterParameterGroup: 
    Type: "AWS::Redshift::ClusterParameterGroup"
    Properties: 
      Parameters: 
        - 
          ParameterName: "require_ssl"
          ParameterValue: "true"

All of your observational health data is stored inside the OHDSI Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). This model also stores useful vocabulary tables that help to translate values from various data sources (like EHR systems and claims data).

The OMOP CDM schema is deployed onto Amazon Redshift. Amazon Redshift is a fast, fully managed data warehouse that allows you to run complex analytic queries against petabytes of structured data. It uses using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution. You can also resize an Amazon Redshift cluster as your requirements for it change.

The solution in this blog post automatically loads de-identified sample data of 1,000 people from the CMS 2008–2010 Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF). The data has helpful formatting from LTS Computing LLC. Vocabulary data from the OHDSI Athena project is also loaded into the OMOP CDM, and a results set is computed by OHDSI Achilles.

Application Component	AWS Service	Source Link
Automation	AWS CloudFormation	https://github.com/awslabs/aws-ohdsi-automated-deployment
Atlas Application	AWS Elastic Beanstalk	https://github.com/OHDSI/Atlas
WebAPI Web Services	AWS Elastic Beanstalk	https://github.com/OHDSI/WebAPI
WebAPI Database Schema	Amazon Relational Database Service (Amazon RDS) Aurora PostgreSQL	http://www.ohdsi.org/web/wiki/doku.php?id=documentation:software:webapi:postgresql_installation_guide
OMOP CDM v5.2 Database Schema	Amazon Redshift	https://github.com/OHDSI/CommonDataModel/tree/master/Redshift
CMS DE-SynPUF Sample Data	Amazon Redshift	https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/SynPUFs/DE_Syn_PUF.html
Athena Vocabulary Data	Amazon Redshift	http://athena.ohdsi.org/
Achilles Results Computation	Amazon Redshift	https://github.com/OHDSI/Achilles

Following is a detailed technical diagram showing the configuration of the architecture to be deployed.

Deploying OHDSI on AWS

Everything just described is automatically deployed by using an AWS CloudFormation template. Using this template, you can quickly get started with the OHDSI project. The CloudFormation templates for this deployment as well as all of the supporting scripts and source code can be found in the AWS Labs GitHub repo.

From your AWS account, open the CloudFormation Management Console and choose Create Stack. From there, copy and paste the following URL in the Specify an Amazon S3 template URL box, and choose Next.

https://s3.amazonaws.com/aws-bigdata-blog/artifacts/aws-ohdsi-automated-deployment/OHDSI-master.yaml

On the next screen, you provide a Stack Name (this can be anything you like) and a few other parameters for your OHDSI environment.

You use the DatabasePassword parameter to set the password for the master user account of the Amazon Redshift and Aurora databases.

You use the EBEndpoint name to generate a unique URL for Atlas to access the OHDSI environment. It is http://EBEndpoint.AWS-Region.elasticbeanstalk.com, where EBEndpoint.AWS-Region indicates the Elastic Beanstalk endpoint and AWS Region. You can configure this URL through Elastic Beanstalk if you want to change it in the future.

You use the KPair option to choose one of your existing Amazon EC2 key pairs to use with the instances that Elastic Beanstalk deploys. By doing this, you can gain administrative access to these instances in the future if you need to. If you don’t already have an Amazon EC2 key pair, you can generate one for free. You do this by going to the Key Pairs section of the EC2 console and choosing Generate Key Pair.

Finally, you use the UserIPRange parameter to specify a CIDR IP address range from which to access your OHDSI environment. By default, your OHDSI environment is accessible over the public internet. Use UserIPRange to limit access over the Internet to a single IP address or a range of IP addresses that represent users you want to have access. Through additional configuration, you can also make your OHDSI environment completely private and accessible only through a VPN or AWS Direct Connect private circuit.

When you’ve provided all Parameters, choose Next.

On the next screen, you can provide some other optional information like tags at your discretion, or just choose Next.

On the next screen, you can review what will be deployed. At the bottom of the screen, there is a check box for you to acknowledge that AWS CloudFormation might create IAM resources with custom names. This is correct; the template being deployed creates four custom roles that give permission for the AWS services involved to communicate with each other. Details of these permissions are inside the CloudFormation template referenced in the URL given in the first step. Check the box acknowledging this and choose Next.

You can watch as CloudFormation builds out your OHDSI architecture. A CloudFormation deployment is called a stack. The parent stack creates two child stacks, one containing the VPC and IAM roles and another created by Elastic Beanstalk with the Atlas and WebAPI servers. When all three stacks have reached the green CREATE_COMPLETE status, as shown in the screenshot following, then the OHDSI architecture has been deployed.

There is still some work going on behind the scenes, though. To watch the progress, browse to the Amazon Redshift section of your AWS Management Console and choose the Amazon Redshift cluster that was created for your OHDSI architecture. After you do so, you can observe the Loads and Queries tabs.

First, on the Loads tab, you can see the CMS De-SynPUF sample data and Athena vocabulary data being loaded into the OMOP Common Data Model. After you see the VOCABULARY table reach the COMPLETED status (as shown following), all of the sample and vocabulary data has been loaded.

After the data loads, the Achilles computation starts. On the Queries tab, you can watch Achilles running queries against your database to build out the Results schema. Achilles runs a large number of queries, and the entire process can take quite some time (about 20 minutes for the sample data we’ve loaded). Eventually, no new queries show up in the Queries tab, which shows that the Achilles computation is completed. The entire process from the time you executed the CloudFormation template until the Achilles computation is completed usually takes about an hour and 15 minutes.

At this point, you can browse to the Elastic Beanstalk section of the AWS Management Console. There, you can choose the OHDSI Application and Environment (green box) that was deployed by the CloudFormation template. At the top of the dashboard, as shown following, you see a link to a URL. This URL matches the name you provided in the EBEndpoint parameter of the CloudFormation template. Choose this URL, and you can start using Atlas to explore the CMS DE-SynPUF sample data!

Cost of deploying this environment

It used to be common to see healthcare data analytics environments deployed in an on-premises data center with expensive data warehouse appliances and virtualized environments. The cloud era has democratized the availability of the infrastructure required to do this type of data analysis, so that now it is within reach of even small organizations. This environment can expand to analyze petabyte-scale health data, and you only pay for what you need. See an estimated breakdown of the monthly cost components for this environment as deployed on the AWS Solution Calculator.

It’s also worth noting that this environment does not have to be run all of the time. If you are only performing analyses periodically, you can terminate the environment when you are finished and restore it from the database backups when you want to continue working. This would reduce the cost of operation even further.

Summary

Now that you have a fully functional OHDSI environment with sample data, you can use this to explore and learn the toolset and its capabilities. After learning with the sample data, you can begin gaining insights by analyzing your own organization’s health data. You can do this using an extract, transform, load (ETL) process from one or more of your health data sources.

Additional Reading

If you found this post useful, be sure to check out Build a Healthcare Data Warehouse Using Amazon EMR, Amazon Redshift, AWS Lambda, and OMOP for info on how to automate data ETL to the OMOP CDM.

About the Author

James Wiggins is a senior healthcare solutions architect at AWS. He is passionate about using technology to help organizations positively impact world health. He also loves spending time with his wife and three children.

[$] A cyborg’s journey

2018-02-07 jake

Post Syndicated from jake original https://lwn.net/Articles/745942/rss

Karen Sandler has been giving conference talks about free software and open
medical devices
for the better part of a decade at this point. LWN briefly covered a 2010 LinuxCon talk and a 2012 linux.conf.au (LCA) talk; her talk at
LCA 2012 was her first full-length keynote, she said. In this year’s
edition, she
reviewed her history (including her love for LCA based in part on that 2012
visit)
and gave an update on the status of the source code for the device she
has implanted on her heart.

Huge Rightsholder Coalition Calls on New EU Presidency to Remove Safe Harbors

2018-01-31 Andy

Post Syndicated from Andy original https://torrentfreak.com/huge-rightsholder-coalition-calls-on-new-eu-presidency-to-remove-safe-harbors-180131/

While piracy of all kinds is often viewed as a threat to the creative industries, a new type of unauthorized content distribution has been gaining prominence over the past few years.

Sites like YouTube, that allow their users to upload all kinds of material – some of it infringing – are now seen as undermining a broad range of industries that rely on both video and audio to generate revenue.

The cries against such User Uploaded Content (UUC) sites are often led by the music industry, which complains that the safe harbor provisions of copyright law are being abused while UUC sites generate review from infringing content. In tandem, while that free content is made available, UUC sites have little or no incentive to pay for official content licenses, and certainly not at a rate considered fair by the industry.

This mismatch, between the price that content industries would like to achieve for licenses and what they actually achieve, is now known as the ‘Value Gap’.

Today, in advance of an EU meeting on the draft Copyright Directive, a huge coalition of rightsholder groups is calling on the new EU Presidency not to pass up an “unmissable opportunity” to find a solution to their problems.

In a letter addressed to the Presidency of the Council of the European Union, which Bulgaria officially took over January 1, 2018, an army of rightsholders lay out their demands.

“We represent musical, audio-visual, literary, visual authors; performers; book, press, musical, scientific, technical and medical publishers; recorded music, film and TV producers; football leagues; broadcasters; distributors and photo agencies. These are at the very heart of Europe’s creative sector,” the groups write.

“We have formed an alliance to campaign for a solution to a major problem which is holding back our sector and jeopardizing future sustainability – the Transfer of Value, otherwise known as the Value Gap.

“User uploaded content services have become vast distributors of our creative works e.g. film, music, photos, broadcasts, text and sport content – all while refusing to negotiate fair or any copyright licences with us as right holders.”

Value Gap Coalition

Featuring groups representing many thousands of rightsholders, the coalition is the broadest yet to call for action against the ‘Value Gap’. Or, to put it another way, to demand a change in the law to prevent sites like YouTube, Facebook and other hosting platforms from “hiding” behind provisions designed to protect them from the infringing activities of others.

“This problem is caused by a lack of clarity surrounding the application of copyright to certain online services and the abuse of European copyright ‘safe harbor’ rules in the e-Commerce Directive (2000/31/EC) by those services,” the coalition writes.

Referencing the EU Copyright Directive proposal tabled by the European Commission in September 2016, the coalition says that UUC services communicating content to the public should be compelled to obtain licenses for that content. If they play an “active role” through promotion or optimization of content, UUC platforms should be denied ‘safe harbors’ under copyright law, they argue.

Noting that there is “no solution” to the problem without the above fixes, the coalition cites last year’s ruling by the Court of Justice of the European Union which found that The Pirate Bay knowingly provide users with a platform to share copyright-infringing links.

“It is important to recall that the underlying policy objective of this legislation is to address the current unfairness in the online market due to the misapplication of copyright liability rules by UUC services. We would therefore like to stress that the focus should remain on finding effective solutions to tackle this issue.

“As an alliance, we look forward to working with your Presidency to achieve an effective solution to the Value Gap problem for the benefit of Europe,” the coalition concludes.

The letter, addressed to Prime Minister Borissov, Minister Pavlova and Minister Banov, arrives in the wake of an alert sounded by several Members of the European Parliament.

Earlier this month they warned that the EU’s proposed mandatory upload filters – which could see UUC sites pre-screen user-uploaded content for infringement – amount to “censorship machines” that will do more harm than good.

The full letter can be found here (pdf)

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

The Effects of the Spectre and Meltdown Vulnerabilities

2018-01-26 Bruce Schneier

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/01/the_effects_of_3.html

On January 3, the world learned about a series of major security vulnerabilities in modern microprocessors. Called Spectre and Meltdown, these vulnerabilities were discovered by several different researchers last summer, disclosed to the microprocessors’ manufacturers, and patched — at least to the extent possible.

This news isn’t really any different from the usual endless stream of security vulnerabilities and patches, but it’s also a harbinger of the sorts of security problems we’re going to be seeing in the coming years. These are vulnerabilities in computer hardware, not software. They affect virtually all high-end microprocessors produced in the last 20 years. Patching them requires large-scale coordination across the industry, and in some cases drastically affects the performance of the computers. And sometimes patching isn’t possible; the vulnerability will remain until the computer is discarded.

Spectre and Meltdown aren’t anomalies. They represent a new area to look for vulnerabilities and a new avenue of attack. They’re the future of security — and it doesn’t look good for the defenders.

Modern computers do lots of things at the same time. Your computer and your phone simultaneously run several applications — or apps. Your browser has several windows open. A cloud computer runs applications for many different computers. All of those applications need to be isolated from each other. For security, one application isn’t supposed to be able to peek at what another one is doing, except in very controlled circumstances. Otherwise, a malicious advertisement on a website you’re visiting could eavesdrop on your banking details, or the cloud service purchased by some foreign intelligence organization could eavesdrop on every other cloud customer, and so on. The companies that write browsers, operating systems, and cloud infrastructure spend a lot of time making sure this isolation works.

Both Spectre and Meltdown break that isolation, deep down at the microprocessor level, by exploiting performance optimizations that have been implemented for the past decade or so. Basically, microprocessors have become so fast that they spend a lot of time waiting for data to move in and out of memory. To increase performance, these processors guess what data they’re going to receive and execute instructions based on that. If the guess turns out to be correct, it’s a performance win. If it’s wrong, the microprocessors throw away what they’ve done without losing any time. This feature is called speculative execution.

Spectre and Meltdown attack speculative execution in different ways. Meltdown is more of a conventional vulnerability; the designers of the speculative-execution process made a mistake, so they just needed to fix it. Spectre is worse; it’s a flaw in the very concept of speculative execution. There’s no way to patch that vulnerability; the chips need to be redesigned in such a way as to eliminate it.

Since the announcement, manufacturers have been rolling out patches to these vulnerabilities to the extent possible. Operating systems have been patched so that attackers can’t make use of the vulnerabilities. Web browsers have been patched. Chips have been patched. From the user’s perspective, these are routine fixes. But several aspects of these vulnerabilities illustrate the sorts of security problems we’re only going to be seeing more of.

First, attacks against hardware, as opposed to software, will become more common. Last fall, vulnerabilities were discovered in Intel’s Management Engine, a remote-administration feature on its microprocessors. Like Spectre and Meltdown, they affected how the chips operate. Looking for vulnerabilities on computer chips is new. Now that researchers know this is a fruitful area to explore, security researchers, foreign intelligence agencies, and criminals will be on the hunt.

Second, because microprocessors are fundamental parts of computers, patching requires coordination between many companies. Even when manufacturers like Intel and AMD can write a patch for a vulnerability, computer makers and application vendors still have to customize and push the patch out to the users. This makes it much harder to keep vulnerabilities secret while patches are being written. Spectre and Meltdown were announced prematurely because details were leaking and rumors were swirling. Situations like this give malicious actors more opportunity to attack systems before they’re guarded.

Third, these vulnerabilities will affect computers’ functionality. In some cases, the patches for Spectre and Meltdown result in significant reductions in speed. The press initially reported 30%, but that only seems true for certain servers running in the cloud. For your personal computer or phone, the performance hit from the patch is minimal. But as more vulnerabilities are discovered in hardware, patches will affect performance in noticeable ways.

And then there are the unpatchable vulnerabilities. For decades, the computer industry has kept things secure by finding vulnerabilities in fielded products and quickly patching them. Now there are cases where that doesn’t work. Sometimes it’s because computers are in cheap products that don’t have a patch mechanism, like many of the DVRs and webcams that are vulnerable to the Mirai (and other) botnets — groups of Internet-connected devices sabotaged for coordinated digital attacks. Sometimes it’s because a computer chip’s functionality is so core to a computer’s design that patching it effectively means turning the computer off. This, too, is becoming more common.

Increasingly, everything is a computer: not just your laptop and phone, but your car, your appliances, your medical devices, and global infrastructure. These computers are and always will be vulnerable, but Spectre and Meltdown represent a new class of vulnerability. Unpatchable vulnerabilities in the deepest recesses of the world’s computer hardware is the new normal. It’s going to leave us all much more vulnerable in the future.

This essay previously appeared on TheAtlantic.com.

US Govt Brands Torrent, Streaming & Cyberlocker Sites As Notorious Markets

2018-01-15 Andy

Post Syndicated from Andy original https://torrentfreak.com/us-govt-brands-torrent-streaming-cyberlocker-sites-as-notorious-markets-180115/

In its annual “Out-of-Cycle Review of Notorious Markets” the office of the United States Trade Representative (USTR) has listed a long list of websites said to be involved in online piracy.

The list is compiled with high-level input from various trade groups, including the MPAA and RIAA who both submitted their recommendations (1,2) during early October last year.

With the word “allegedly” used more than two dozen times in the report, the US government notes that its report does not constitute cast-iron proof of illegal activity. However, it urges the countries from where the so-called “notorious markets” operate to take action where they can, while putting owners and facilitators on notice that their activities are under the spotlight.

“A goal of the List is to motivate appropriate action by owners, operators, and service providers in the private sector of these and similar markets, as well as governments, to reduce piracy and counterfeiting,” the report reads.

“USTR highlights the following marketplaces because they exemplify global counterfeiting and piracy concerns and because the scale of infringing activity in these marketplaces can cause significant harm to U.S. intellectual property (IP) owners, consumers, legitimate online platforms, and the economy.”

The report begins with a page titled “Issue Focus: Illicit Streaming Devices”. Unsurprisingly, particularly given their place in dozens of headlines last year, the segment focus on the set-top box phenomenon. The piece doesn’t list any apps or software tools as such but highlights the general position, claiming a cost to the US entertainment industry of $4-5 billion a year.

Torrent Sites

In common with previous years, the USTR goes on to list several of the world’s top torrent sites but due to changes in circumstances, others have been delisted. ExtraTorrent, which shut down May 2017, is one such example.

As the world’s most famous torrent site, The Pirate Bay gets a prominent mention, with the USTR noting that the site is of “symbolic importance as one of the longest-running and most vocal torrent sites. The USTR underlines the site’s resilience by noting its hydra-like form while revealing an apparent secret concerning its hosting arrangements.

“The Pirate Bay has allegedly had more than a dozen domains hosted in various countries around the world, applies a reverse proxy service, and uses a hosting provider in Vietnam to evade further enforcement action,” the USTR notes.

Other torrent sites singled out for criticism include RARBG, which was nominated for the listing by the movie industry. According to the USTR, the site is hosted in Bosnia and Herzegovina and has changed hosting services to prevent shutdowns in recent years.

1337x.to and the meta-search engine Torrentz2 are also given a prime mention, with the USTR noting that they are “two of the most popular torrent sites that allegedly infringe U.S. content industry’s copyrights.” Russia’s RuTracker is also targeted for criticism, with the government noting that it’s now one of the most popular torrent sites in the world.

Streaming & Cyberlockers

While torrent sites are still important, the USTR reserves considerable space in its report for streaming portals and cyberlocker-type services.

4Shared.com, a file-hosting site that has been targeted by dozens of millions of copyright notices, is reportedly no longer able to use major US payment providers. Nevertheless, the British Virgin Islands company still collects significant sums from premium accounts, advertising, and offshore payment processors, USTR notes.

Cyberlocker Rapidgator gets another prominent mention in 2017, with the USTR noting that the Russian-hosted platform generates millions of dollars every year through premium memberships while employing rewards and affiliate schemes.

Due to its increasing popularity as a hosting and streaming operation, Openload.co (Romania) is now a big target for the USTR. “The site is used frequently in combination with add-ons in illicit streaming devices. In November 2017, users visited Openload.co a staggering 270 million times,” the USTR writes.

Owned by a Swiss company and hosted in the Netherlands, the popular site Uploaded is also criticized by the US alongside France’s 1Fichier.com, which allegedly hosts pirate games while being largely unresponsive to takedown notices. Dopefile.pk, a Pakistan-based storage outfit, is also highlighted.

On the video streaming front, it’s perhaps no surprise that the USTR focuses on sites like FMovies (Sweden), GoStream (Vietnam), Movie4K.tv (Russia) and PrimeWire. An organization collectively known as the MovShare group which encompasses Nowvideo.sx, WholeCloud.net, NowDownload.cd, MeWatchSeries.to and WatchSeries.ac, among others, is also listed.

Unauthorized music / research papers

While most of the above are either focused on video or feature it as part of their repertoire, other sites are listed for their attention to music. Convert2MP3.net is named as one of the most popular stream-ripping sites in the world and is highlighted due to the prevalence of YouTube-downloader sites and the 2017 demise of YouTube-MP3.

“Convert2MP3.net does not appear to have permission from YouTube or other sites and does not have permission from right holders for a wide variety of music represented by major U.S. labels,” the USTR notes.

Given the amount of attention the site has received in 2017 as ‘The Pirate Bay of Research’, Libgen.io and Sci-Hub.io (not to mention the endless proxy and mirror sites that facilitate access) are given a detailed mention in this year’s report.

“Together these sites make it possible to download — all without permission and without remunerating authors, publishers or researchers — millions of copyrighted books by commercial publishers and university presses; scientific, technical and medical journal articles; and publications of technological standards,” the USTR writes.

Service providers

But it’s not only sites that are being put under pressure. Following a growing list of nominations in previous years, Swiss service provider Private Layer is again singled out as a rogue player in the market for hosting 1337x.to and Torrentz2.eu, among others.

“While the exact configuration of websites changes from year to year, this is the fourth consecutive year that the List has stressed the significant international trade impact of Private Layer’s hosting services and the allegedly infringing sites it hosts,” the USTR notes.

“Other listed and nominated sites may also be hosted by Private Layer but are using
reverse proxy services to obfuscate the true host from the public and from law enforcement.”

The USTR notes Switzerland’s efforts to close a legal loophole that restricts enforcement and looks forward to a positive outcome when the draft amendment is considered by parliament.

Perhaps a little surprisingly given its recent anti-piracy efforts and overtures to the US, Russia’s leading social network VK.com again gets a place on the new list. The USTR recognizes VK’s efforts but insists that more needs to be done.

Social networking and e-commerce

“In 2016, VK reached licensing agreements with major record companies, took steps to limit third-party applications dedicated to downloading infringing content from the site, and experimented with content recognition technologies,” the USTR writes.

“Despite these positive signals, VK reportedly continues to be a hub of infringing activity and the U.S. motion picture industry reports that they find thousands of infringing files on the site each month.”

Finally, in addition to traditional pirate sites, the US also lists online marketplaces that allegedly fail to meet appropriate standards. Re-added to the list in 2016 after a brief hiatus in 2015, China’s Alibaba is listed again in 2017. The development provoked an angry response from the company.

Describing his company as a “scapegoat”, Alibaba Group President Michael Evans said that his platform had achieved a 25% drop in takedown requests and has even been removing infringing listings before they make it online.

“In light of all this, it’s clear that no matter how much action we take and progress we make, the USTR is not actually interested in seeing tangible results,” Evans said in a statement.

The full list of sites in the Notorious Markets Report 2017 (pdf) can be found below.

– 1fichier.com – (cyberlocker)
– 4shared.com – (cyberlocker)
– convert2mp3.net – (stream-ripper)
– Dhgate.com (e-commerce)
– Dopefile.pl – (cyberlocker)
– Firestorm-servers.com (pirate gaming service)
– Fmovies.is, Fmovies.se, Fmovies.to – (streaming)
– Gostream.is, Gomovies.to, 123movieshd.to (streaming)
– Indiamart.com (e-commerce)
– Kinogo.club, kinogo.co (streaming host, platform)
– Libgen.io, sci-hub.io, libgen.pw, sci-hub.cc, sci-hub.bz, libgen.info, lib.rus.ec, bookfi.org, bookzz.org, booker.org, booksc.org, book4you.org, bookos-z1.org, booksee.org, b-ok.org (research downloads)
– Movshare Group – Nowvideo.sx, wholecloud.net, auroravid.to, bitvid.sx, nowdownload.ch, cloudtime.to, mewatchseries.to, watchseries.ac (streaming)
– Movie4k.tv (streaming)
– MP3VA.com (music)
– Openload.co (cyberlocker / streaming)
– 1337x.to (torrent site)
– Primewire.ag (streaming)
– Torrentz2, Torrentz2.me, Torrentz2.is (torrent site)
– Rarbg.to (torrent site)
– Rebel (domain company)
– Repelis.tv (movie and TV linking)
– RuTracker.org (torrent site)
– Rapidgator.net (cyberlocker)
– Taobao.com (e-commerce)
– The Pirate Bay (torrent site)
– TVPlus, TVBrowser, Kuaikan (streaming apps and addons, China)
– Uploaded.net (cyberlocker)
– VK.com (social networking)

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Spectre and Meltdown Attacks Against Microprocessors

2018-01-05 Bruce Schneier

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/01/spectre_and_mel_1.html

The security of pretty much every computer on the planet has just gotten a lot worse, and the only real solution — which of course is not a solution — is to throw them all away and buy new ones.

On Wednesday, researchers just announced a series of major security vulnerabilities in the microprocessors at the heart of the world’s computers for the past 15-20 years. They’ve been named Spectre and Meltdown, and they have to do with manipulating different ways processors optimize performance by rearranging the order of instructions or performing different instructions in parallel. An attacker who controls one process on a system can use the vulnerabilities to steal secrets elsewhere on the computer. (The research papers are here and here.)

This means that a malicious app on your phone could steal data from your other apps. Or a malicious program on your computer — maybe one running in a browser window from that sketchy site you’re visiting, or as a result of a phishing attack — can steal data elsewhere on your machine. Cloud services, which often share machines amongst several customers, are especially vulnerable. This affects corporate applications running on cloud infrastructure, and end-user cloud applications like Google Drive. Someone can run a process in the cloud and steal data from every other users on the same hardware.

Information about these flaws has been secretly circulating amongst the major IT companies for months as they researched the ramifications and coordinated updates. The details were supposed to be released next week, but the story broke early and everyone is scrambling. By now all the major cloud vendors have patched their systems against the vulnerabilities that can be patched against.

“Throw it away and buy a new one” is ridiculous security advice, but it’s what US-CERT recommends. It is also unworkable. The problem is that there isn’t anything to buy that isn’t vulnerable. Pretty much every major processor made in the past 20 years is vulnerable to some flavor of these vulnerabilities. Patching against Meltdown can degrade performance by almost a third. And there’s no patch for Spectre; the microprocessors have to be redesigned to prevent the attack, and that will take years. (Here’s a running list of who’s patched what.)

This is bad, but expect it more and more. Several trends are converging in a way that makes our current system of patching security vulnerabilities harder to implement.

The first is that these vulnerabilities affect embedded computers in consumer devices. Unlike our computer and phones, these systems are designed and produced at a lower profit margin with less engineering expertise. There aren’t security teams on call to write patches, and there often aren’t mechanisms to push patches onto the devices. We’re already seeing this with home routers, digital video recorders, and webcams. The vulnerability that allowed them to be taken over by the Mirai botnet last August simply can’t be fixed.

The second is that some of the patches require updating the computer’s firmware. This is much harder to walk consumers through, and is more likely to permanently brick the device if something goes wrong. It also requires more coordination. In November, Intel released a firmware update to fix a vulnerability in its Management Engine (ME): another flaw in its microprocessors. But it couldn’t get that update directly to users; it had to work with the individual hardware companies, and some of them just weren’t capable of getting the update to their customers.

We’re already seeing this. Some patches require users to disable the computer’s password, which means organizations can’t automate the patch. Some antivirus software blocks the patch, or — worse — crashes the computer. This results in a three-step process: patch your antivirus software, patch your operating system, and then patch the computer’s firmware.

The final reason is the nature of these vulnerabilities themselves. These aren’t normal software vulnerabilities, where a patch fixes the problem and everyone can move on. These vulnerabilities are in the fundamentals of how the microprocessor operates.

It shouldn’t be surprising that microprocessor designers have been building insecure hardware for 20 years. What’s surprising is that it took 20 years to discover it. In their rush to make computers faster, they weren’t thinking about security. They didn’t have the expertise to find these vulnerabilities. And those who did were too busy finding normal software vulnerabilities to examine microprocessors. Security researchers are starting to look more closely at these systems, so expect to hear about more vulnerabilities along these lines.

Spectre and Meltdown are pretty catastrophic vulnerabilities, but they only affect the confidentiality of data. Now that they — and the research into the Intel ME vulnerability — have shown researchers where to look, more is coming — and what they’ll find will be worse than either Spectre or Meltdown. There will be vulnerabilities that will allow attackers to manipulate or delete data across processes, potentially fatal in the computers controlling our cars or implanted medical devices. These will be similarly impossible to fix, and the only strategy will be to throw our devices away and buy new ones.

This isn’t to say you should immediately turn your computers and phones off and not use them for a few years. For the average user, this is just another attack method amongst many. All the major vendors are working on patches and workarounds for the attacks they can mitigate. All the normal security advice still applies: watch for phishing attacks, don’t click on strange e-mail attachments, don’t visit sketchy websites that might run malware on your browser, patch your systems regularly, and generally be careful on the Internet.

You probably won’t notice that performance hit once Meltdown is patched, except maybe in backup programs and networking applications. Embedded systems that do only one task, like your programmable thermostat or the computer in your refrigerator, are unaffected. Small microprocessors that don’t do all of the vulnerable fancy performance tricks are unaffected. Browsers will figure out how to mitigate this in software. Overall, the security of the average Internet-of-Things device is so bad that this attack is in the noise compared to the previously known risks.

It’s a much bigger problem for cloud vendors; the performance hit will be expensive, but I expect that they’ll figure out some clever way of detecting and blocking the attacks. All in all, as bad as Spectre and Meltdown are, I think we got lucky.

But more are coming, and they’ll be worse. 2018 will be the year of microprocessor vulnerabilities, and it’s going to be a wild ride.

Note: A shorter version of this essay previously appeared on CNN.com. My previous blog post on this topic contains additional links.

2017-12-27 34c3 ден 1

2017-12-28 Vasil Kolev

Post Syndicated from Vasil Kolev original https://vasil.ludost.net/blog/?p=3373

Успявам да гледам малко лекции от 34c3 (програма, streaming).

Откриването на Charlie Stross (който ми е от любимите автори) беше доста интересно, с наблюдението, че корпорациите могат да се разглеждат като начална форма на изкуствените интелекти и всякакви интересни следствия от това, струва си да се отдели малко време и да се гледа (не знам дали ще го качи в блога си).

Лекцията за геймифицираната система за социален кредит в Китай не ми каза нещо ново и не беше особено добре представена, но е добре човек да почете за ситуацията.

Харалд Велте разказа за internet-а и BBS-ите от едно време (само че в Германия), като цяло все неща, с които едно време сме си играли. Иво ме пита дали не можем да направим някаква такава лекция или да намерим история на случвалите се неща в България. Мислех си, че вече има такова нещо, ама не мога да го намеря, някой да се сеща за хубава история на ония времена?

Лекцията за Иран имаше малко полезна информация в нея, но основно не си заслужаваше. Лекцията за Саудитска Арабия също нямаше много съдържание.

Лекцията за “Low Cost Non-Invasive Biomedical Imaging” за момента ми е любима, и трябва да си вземем едно такова нещо за в лаба. Звучи като технология, с която си струва да си играем и която може много да подобри работата на всякакви лекари.

“Defeating (Not)Petya’s Cryptography” имаше полезни моменти.

Като успея да изгледам още някакви неща, ще пиша и за тях. Който иска, може директно да ходи в initLab да гледа, тъкмо ще има с кой да коментира 🙂

Update: “The Ultimate Apollo Guidance Computer Talk” се оказа страхотно, особено архитектурата на нещото, която има вид на скалъпена с тел и тиксо.

Acoustical Attacks against Hard Drives

2017-12-26 Bruce Schneier

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/12/acoustical_atta.html

Interesting destructive attack: “Acoustic Denial of Service Attacks on HDDs“:

Abstract: Among storage components, hard disk drives (HDDs) have become the most commonly-used type of non-volatile storage due to their recent technological advances, including, enhanced energy efficacy and significantly-improved areal density. Such advances in HDDs have made them an inevitable part of numerous computing systems, including, personal computers, closed-circuit television (CCTV) systems, medical bedside monitors, and automated teller machines (ATMs). Despite the widespread use of HDDs and their critical role in real-world systems, there exist only a few research studies on the security of HDDs. In particular, prior research studies have discussed how HDDs can potentially leak critical private information through acoustic or electromagnetic emanations. Borrowing theoretical principles from acoustics and mechanics, we propose a novel denial-of-service (DoS) attack against HDDs that exploits a physical phenomenon, known as acoustic resonance. We perform a comprehensive examination of physical characteristics of several HDDs and create acoustic signals that cause significant vibrations in HDDs internal components. We demonstrate that such vibrations can negatively influence the performance of HDDs embedded in real-world systems. We show the feasibility of the proposed attack in two real-world case studies, namely, personal computers and CCTVs.

Warrant Protections against Police Searches of Our Data

2017-11-29 Bruce Schneier

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/11/warrant_protect.html

The cell phones we carry with us constantly are the most perfect surveillance device ever invented, and our laws haven’t caught up to that reality. That might change soon.

This week, the Supreme Court will hear a case with profound implications on your security and privacy in the coming years. The Fourth Amendment’s prohibition of unlawful search and seizure is a vital right that protects us all from police overreach, and the way the courts interpret it is increasingly nonsensical in our computerized and networked world. The Supreme Court can either update current law to reflect the world, or it can further solidify an unnecessary and dangerous police power.

The case centers on cell phone location data and whether the police need a warrant to get it, or if they can use a simple subpoena, which is easier to obtain. Current Fourth Amendment doctrine holds that you lose all privacy protections over any data you willingly share with a third party. Your cellular provider, under this interpretation, is a third party with whom you’ve willingly shared your movements, 24 hours a day, going back months — even though you don’t really have any choice about whether to share with them. So police can request records of where you’ve been from cell carriers without any judicial oversight. The case before the court, Carpenter v. United States, could change that.

Traditionally, information that was most precious to us was physically close to us. It was on our bodies, in our homes and offices, in our cars. Because of that, the courts gave that information extra protections. Information that we stored far away from us, or gave to other people, afforded fewer protections. Police searches have been governed by the “third-party doctrine,” which explicitly says that information we share with others is not considered private.

The Internet has turned that thinking upside-down. Our cell phones know who we talk to and, if we’re talking via text or e-mail, what we say. They track our location constantly, so they know where we live and work. Because they’re the first and last thing we check every day, they know when we go to sleep and when we wake up. Because everyone has one, they know whom we sleep with. And because of how those phones work, all that information is naturally shared with third parties.

More generally, all our data is literally stored on computers belonging to other people. It’s our e-mail, text messages, photos, Google docs, and more all in the cloud. We store it there not because it’s unimportant, but precisely because it is important. And as the Internet of Things computerizes the rest our lives, even more data will be collected by other people: data from our health trackers and medical devices, data from our home sensors and appliances, data from Internet-connected “listeners” like Alexa, Siri, and your voice-activated television.

All this data will be collected and saved by third parties, sometimes for years. The result is a detailed dossier of your activities more complete than any private investigator – or police officer – could possibly collect by following you around.

The issue here is not whether the police should be allowed to use that data to help solve crimes. Of course they should. The issue is whether that information should be protected by the warrant process that requires the police to have probable cause to investigate you and get approval by a court.

Warrants are a security mechanism. They prevent the police from abusing their authority to investigate someone they have no reason to suspect of a crime. They prevent the police from going on “fishing expeditions.” They protect our rights and liberties, even as we willingly give up our privacy to the legitimate needs of law enforcement.

The third-party doctrine never made a lot of sense. Just because I share an intimate secret with my spouse, friend, or doctor doesn’t mean that I no longer consider it private. It makes even less sense in today’s hyper-connected world. It’s long past time the Supreme Court recognized that a months’-long history of my movements is private, and my e-mails and other personal data deserve the same protections, whether they’re on my laptop or on Google’s servers.

This essay previously appeared in the Washington Post.

Details on the case. Two opinion pieces.

I signed on to two amicus briefs on the case.

EDITED TO ADD (12/1): Good commentary on the Supreme Court oral arguments.

AWS Big Data & Analytics Sessions at Re:Invent 2017

2017-11-24 Roy Ben-Alta

Post Syndicated from Roy Ben-Alta original https://aws.amazon.com/blogs/big-data/aws-big-data-analytics-sessions-at-reinvent-2017/

We can’t believe that there are just few days left before re:Invent 2017. If you are attending this year, you’ll want to check out our Big Data sessions! The Big Data and Machine Learning categories are bigger than ever. As in previous years, you can find these sessions in various tracks, including Analytics & Big Data, Deep Learning Summit, Artificial Intelligence & Machine Learning, Architecture, and Databases.

We have great sessions from organizations and companies like Vanguard, Cox Automotive, Pinterest, Netflix, FINRA, Amtrak, AmazonFresh, Sysco Foods, Twilio, American Heart Association, Expedia, Esri, Nextdoor, and many more. All sessions are recorded and made available on YouTube. In addition, all slide decks from the sessions will be available on SlideShare.net after the conference.

This post highlights the sessions that will be presented as part of the Analytics & Big Data track, as well as relevant sessions from other tracks like Architecture, Artificial Intelligence & Machine Learning, and IoT. If you’re interested in Machine Learning sessions, don’t forget to check out our Guide to Machine Learning at re:Invent 2017.

This year’s session catalog contains the following breakout sessions.

DAT201 – AWS Database and Analytics State of the Union – Thursday

Raju Gulabani, VP, Database, Analytics and AI at AWS will discuss the evolution of database and analytics services in AWS, the new database and analytics services and features we launched this year, and our vision for continued innovation in this space. We are witnessing an unprecedented growth in the amount of data collected, in many different forms. Storage, management, and analysis of this data require database services that scale and perform in ways not possible before. AWS offers a collection of database and other data services—including Amazon Aurora, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Amazon ElastiCache, Amazon Kinesis, and Amazon EMR—to process, store, manage, and analyze data. In this session, we provide an overview of AWS database and analytics services and discuss how customers are using these services today.

Deep dive customer use cases

ABD401 – How Netflix Monitors Applications in Near Real-Time with Amazon Kinesis
Thousands of services work in concert to deliver millions of hours of video streams to Netflix customers every day. These applications vary in size, function, and technology, but they all make use of the Netflix network to communicate. Understanding the interactions between these services is a daunting challenge both because of the sheer volume of traffic and the dynamic nature of deployments. In this session, we first discuss why Netflix chose Kinesis Streams to address these challenges at scale. We then dive deep into how Netflix uses Kinesis Streams to enrich network traffic logs and identify usage patterns in real time. Lastly, we cover how Netflix uses this system to build comprehensive dependency maps, increase network efficiency, and improve failure resiliency. From this session, you will learn how to build a real-time application monitoring system using network traffic logs and get real-time, actionable insights.

SRV319 – How Nextdoor Built a Scalable, Serverless Data Pipeline for Billions of Events per Day

In this session, learn how Nextdoor replaced their home-grown data pipeline based on a topology of Flume nodes with a completely serverless architecture based on Kinesis and Lambda. By making these changes, they improved both the reliability of their data and the delivery times of billions of records of data to their Amazon S3–based data lake and Amazon Redshift cluster. Nextdoor is a private social networking service for neighborhoods.

ABD205 – Taking a Page Out of Ivy Tech’s Book: Using Data for Student Success
Data speaks. Discover how Ivy Tech, the nation’s largest singly accredited community college, uses AWS to gather, analyze, and take action on student behavioral data for the betterment of over 3,100 students. This session outlines the process from inception to implementation across the state of Indiana and highlights how Ivy Tech’s model can be applied to your own complex business problems.

ABD207 – Leveraging AWS to Fight Financial Crime and Protect National Security
Banks aren’t known to share data and collaborate with one another. But that is exactly what the Mid-Sized Bank Coalition of America (MBCA) is doing to fight digital financial crime—and protect national security. Using the AWS Cloud, the MBCA developed a shared data analytics utility that processes terabytes of non-competitive customer account, transaction, and government risk data. The intelligence produced from the data helps banks increase the efficiency of their operations, cut labor and operating costs, and reduce false positive volumes. The collective intelligence also allows greater enforcement of Anti-Money Laundering (AML) regulations by helping members detect internal risks—and identify the challenges to detecting these risks in the first place. This session demonstrates how the AWS Cloud supports the MBCA to deliver advanced data analytics, provide consistent operating models across financial institutions, reduce costs, and strengthen national security.

ABD208 – Cox Automotive Empowered to Scale with Splunk Cloud & AWS and Explores New Innovation with Amazon Kinesis Firehose
In this session, learn how Cox Automotive is using Splunk Cloud for real time visibility into its AWS and hybrid environments to achieve near instantaneous MTTI, reduce auction incidents by 90%, and proactively predict outages. We also introduce a highly anticipated capability that allows you to ingest, transform, and analyze data in real time using Splunk and Amazon Kinesis Firehose to gain valuable insights from your cloud resources. It’s now quicker and easier than ever to gain access to analytics-driven infrastructure monitoring using Splunk Enterprise & Splunk Cloud.

ABD209 – Accelerating the Speed of Innovation with a Data Sciences Data & Analytics Hub at Takeda
Historically, silos of data, analytics, and processes across functions, stages of development, and geography created a barrier to R&D efficiency. Gathering the right data necessary for decision-making was challenging due to issues of accessibility, trust, and timeliness. In this session, learn how Takeda is undergoing a transformation in R&D to increase the speed-to-market of high-impact therapies to improve patient lives. The Data and Analytics Hub was built, with Deloitte, to address these issues and support the efficient generation of data insights for functions such as clinical operations, clinical development, medical affairs, portfolio management, and R&D finance. In the AWS hosted data lake, this data is processed, integrated, and made available to business end users through data visualization interfaces, and to data scientists through direct connectivity. Learn how Takeda has achieved significant time reductions—from weeks to minutes—to gather and provision data that has the potential to reduce cycle times in drug development. The hub also enables more efficient operations and alignment to achieve product goals through cross functional team accountability and collaboration due to the ability to access the same cross domain data.

ABD210 – Modernizing Amtrak: Serverless Solution for Real-Time Data Capabilities
As the nation’s only high-speed intercity passenger rail provider, Amtrak needs to know critical information to run their business such as: Who’s onboard any train at any time? How are booking and revenue trending? Amtrak was faced with unpredictable and often slow response times from existing databases, ranging from seconds to hours; existing booking and revenue dashboards were spreadsheet-based and manual; multiple copies of data were stored in different repositories, lacking integration and consistency; and operations and maintenance (O&M) costs were relatively high. Join us as we demonstrate how Deloitte and Amtrak successfully went live with a cloud-native operational database and analytical datamart for near-real-time reporting in under six months. We highlight the specific challenges and the modernization of architecture on an AWS native Platform as a Service (PaaS) solution. The solution includes cloud-native components such as AWS Lambda for microservices, Amazon Kinesis and AWS Data Pipeline for moving data, Amazon S3 for storage, Amazon DynamoDB for a managed NoSQL database service, and Amazon Redshift for near-real time reports and dashboards. Deloitte’s solution enabled “at scale” processing of 1 million transactions/day and up to 2K transactions/minute. It provided flexibility and scalability, largely eliminate the need for system management, and dramatically reduce operating costs. Moreover, it laid the groundwork for decommissioning legacy systems, anticipated to save at least $1M over 3 years.

ABD211 – Sysco Foods: A Journey from Too Much Data to Curated Insights
In this session, we detail Sysco’s journey from a company focused on hindsight-based reporting to one focused on insights and foresight. For this shift, Sysco moved from multiple data warehouses to an AWS ecosystem, including Amazon Redshift, Amazon EMR, AWS Data Pipeline, and more. As the team at Sysco worked with Tableau, they gained agile insight across their business. Learn how Sysco decided to use AWS, how they scaled, and how they became more strategic with the AWS ecosystem and Tableau.

ABD217 – From Batch to Streaming: How Amazon Flex Uses Real-time Analytics to Deliver Packages on Time
Reducing the time to get actionable insights from data is important to all businesses, and customers who employ batch data analytics tools are exploring the benefits of streaming analytics. Learn best practices to extend your architecture from data warehouses and databases to real-time solutions. Learn how to use Amazon Kinesis to get real-time data insights and integrate them with Amazon Aurora, Amazon RDS, Amazon Redshift, and Amazon S3. The Amazon Flex team describes how they used streaming analytics in their Amazon Flex mobile app, used by Amazon delivery drivers to deliver millions of packages each month on time. They discuss the architecture that enabled the move from a batch processing system to a real-time system, overcoming the challenges of migrating existing batch data to streaming data, and how to benefit from real-time analytics.

ABD218 – How EuroLeague Basketball Uses IoT Analytics to Engage Fans
IoT and big data have made their way out of industrial applications, general automation, and consumer goods, and are now a valuable tool for improving consumer engagement across a number of industries, including media, entertainment, and sports. The low cost and ease of implementation of AWS analytics services and AWS IoT have allowed AGT, a leader in IoT, to develop their IoTA analytics platform. Using IoTA, AGT brought a tailored solution to EuroLeague Basketball for real-time content production and fan engagement during the 2017-18 season. In this session, we take a deep dive into how this solution is architected for secure, scalable, and highly performant data collection from athletes, coaches, and fans. We also talk about how the data is transformed into insights and integrated into a content generation pipeline. Lastly, we demonstrate how this solution can be easily adapted for other industries and applications.

ABD222 – How to Confidently Unleash Data to Meet the Needs of Your Entire Organization
Where are you on the spectrum of IT leaders? Are you confident that you’re providing the technology and solutions that consistently meet or exceed the needs of your internal customers? Do your peers at the executive table see you as an innovative technology leader? Innovative IT leaders understand the value of getting data and analytics directly into the hands of decision makers, and into their own. In this session, Daren Thayne, Domo’s Chief Technology Officer, shares how innovative IT leaders are helping drive a culture change at their organizations. See how transformative it can be to have real-time access to all of the data that’ is relevant to YOUR job (including a complete view of your entire AWS environment), as well as understand how it can help you lead the way in applying that same pattern throughout your entire company

ABD303 – Developing an Insights Platform – Sysco’s Journey from Disparate Systems to Data Lake and Beyond
Sysco has nearly 200 operating companies across its multiple lines of business throughout the United States, Canada, Central/South America, and Europe. As the global leader in food services, Sysco identified the need to streamline the collection, transformation, and presentation of data produced by the distributed units and systems, into a central data ecosystem. Sysco’s Business Intelligence and Analytics team addressed these requirements by creating a data lake with scalable analytics and query engines leveraging AWS services. In this session, Sysco will outline their journey from a hindsight reporting focused company to an insights driven organization. They will cover solution architecture, challenges, and lessons learned from deploying a self-service insights platform. They will also walk through the design patterns they used and how they designed the solution to provide predictive analytics using Amazon Redshift Spectrum, Amazon S3, Amazon EMR, AWS Glue, Amazon Elasticsearch Service and other AWS services.

ABD309 – How Twilio Scaled Its Data-Driven Culture
As a leading cloud communications platform, Twilio has always been strongly data-driven. But as headcount and data volumes grew—and grew quickly—they faced many new challenges. One-off, static reports work when you’re a small startup, but how do you support a growth stage company to a successful IPO and beyond? Today, Twilio’s data team relies on AWS and Looker to provide data access to 700 colleagues. Departments have the data they need to make decisions, and cloud-based scale means they get answers fast. Data delivers real-business value at Twilio, providing a 360-degree view of their customer, product, and business. In this session, you hear firsthand stories directly from the Twilio data team and learn real-world tips for fostering a truly data-driven culture at scale.

ABD310 – How FINRA Secures Its Big Data and Data Science Platform on AWS
FINRA uses big data and data science technologies to detect fraud, market manipulation, and insider trading across US capital markets. As a financial regulator, FINRA analyzes highly sensitive data, so information security is critical. Learn how FINRA secures its Amazon S3 Data Lake and its data science platform on Amazon EMR and Amazon Redshift, while empowering data scientists with tools they need to be effective. In addition, FINRA shares AWS security best practices, covering topics such as AMI updates, micro segmentation, encryption, key management, logging, identity and access management, and compliance.

ABD331 – Log Analytics at Expedia Using Amazon Elasticsearch Service
Expedia uses Amazon Elasticsearch Service (Amazon ES) for a variety of mission-critical use cases, ranging from log aggregation to application monitoring and pricing optimization. In this session, the Expedia team reviews how they use Amazon ES and Kibana to analyze and visualize Docker startup logs, AWS CloudTrail data, and application metrics. They share best practices for architecting a scalable, secure log analytics solution using Amazon ES, so you can add new data sources almost effortlessly and get insights quickly

ABD316 – American Heart Association: Finding Cures to Heart Disease Through the Power of Technology
Combining disparate datasets and making them accessible to data scientists and researchers is a prevalent challenge for many organizations, not just in healthcare research. American Heart Association (AHA) has built a data science platform using Amazon EMR, Amazon Elasticsearch Service, and other AWS services, that corrals multiple datasets and enables advanced research on phenotype and genotype datasets, aimed at curing heart diseases. In this session, we present how AHA built this platform and the key challenges they addressed with the solution. We also provide a demo of the platform, and leave you with suggestions and next steps so you can build similar solutions for your use cases

ABD319 – Tooling Up for Efficiency: DIY Solutions @ Netflix
At Netflix, we have traditionally approached cloud efficiency from a human standpoint, whether it be in-person meetings with the largest service teams or manually flipping reservations. Over time, we realized that these manual processes are not scalable as the business continues to grow. Therefore, in the past year, we have focused on building out tools that allow us to make more insightful, data-driven decisions around capacity and efficiency. In this session, we discuss the DIY applications, dashboards, and processes we built to help with capacity and efficiency. We start at the ten thousand foot view to understand the unique business and cloud problems that drove us to create these products, and discuss implementation details, including the challenges encountered along the way. Tools discussed include Picsou, the successor to our AWS billing file cost analyzer; Libra, an easy-to-use reservation conversion application; and cost and efficiency dashboards that relay useful financial context to 50+ engineering teams and managers.

ABD312 – Deep Dive: Migrating Big Data Workloads to AWS
Customers are migrating their analytics, data processing (ETL), and data science workloads running on Apache Hadoop, Spark, and data warehouse appliances from on-premise deployments to AWS in order to save costs, increase availability, and improve performance. AWS offers a broad set of analytics services, including solutions for batch processing, stream processing, machine learning, data workflow orchestration, and data warehousing. This session will focus on identifying the components and workflows in your current environment; and providing the best practices to migrate these workloads to the right AWS data analytics product. We will cover services such as Amazon EMR, Amazon Athena, Amazon Redshift, Amazon Kinesis, and more. We will also feature Vanguard, an American investment management company based in Malvern, Pennsylvania with over $4.4 trillion in assets under management. Ritesh Shah, Sr. Program Manager for Cloud Analytics Program at Vanguard, will describe how they orchestrated their migration to AWS analytics services, including Hadoop and Spark workloads to Amazon EMR. Ritesh will highlight the technical challenges they faced and overcame along the way, as well as share common recommendations and tuning tips to accelerate the time to production.

ABD402 – How Esri Optimizes Massive Image Archives for Analytics in the Cloud
Petabyte scale archives of satellites, planes, and drones imagery continue to grow exponentially. They mostly exist as semi-structured data, but they are only valuable when accessed and processed by a wide range of products for both visualization and analysis. This session provides an overview of how ArcGIS indexes and structures data so that any part of it can be quickly accessed, processed, and analyzed by reading only the minimum amount of data needed for the task. In this session, we share best practices for structuring and compressing massive datasets in Amazon S3, so it can be analyzed efficiently. We also review a number of different image formats, including GeoTIFF (used for the Public Datasets on AWS program, Landsat on AWS), cloud optimized GeoTIFF, MRF, and CRF as well as different compression approaches to show the effect on processing performance. Finally, we provide examples of how this technology has been used to help image processing and analysis for the response to Hurricane Harvey.

ABD329 – A Look Under the Hood – How Amazon.com Uses AWS Services for Analytics at Massive Scale
Amazon’s consumer business continues to grow, and so does the volume of data and the number and complexity of the analytics done in support of the business. In this session, we talk about how Amazon.com uses AWS technologies to build a scalable environment for data and analytics. We look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel, scalable compute engines such as Amazon EMR and Amazon Redshift.

ABD327 – Migrating Your Traditional Data Warehouse to a Modern Data Lake
In this session, we discuss the latest features of Amazon Redshift and Redshift Spectrum, and take a deep dive into its architecture and inner workings. We share many of the recent availability, performance, and management enhancements and how they improve your end user experience. You also hear from 21st Century Fox, who presents a case study of their fast migration from an on-premises data warehouse to Amazon Redshift. Learn how they are expanding their data warehouse to a data lake that encompasses multiple data sources and data formats. This architecture helps them tie together siloed business units and get actionable 360-degree insights across their consumer base.

MCL202 – Ally Bank & Cognizant: Transforming Customer Experience Using Amazon Alexa
Given the increasing popularity of natural language interfaces such as Voice as User technology or conversational artificial intelligence (AI), Ally® Bank was looking to interact with customers by enabling direct transactions through conversation or voice. They also needed to develop a capability that allows third parties to connect to the bank securely for information sharing and exchange, using oAuth, an authentication protocol seen as the future of secure banking technology. Cognizant’s Architecture team partnered with Ally Bank’s Enterprise Architecture group and identified the right product for oAuth integration with Amazon Alexa and third-party technologies. In this session, we discuss how building products with conversational AI helps Ally Bank offer an innovative customer experience; increase retention through improved data-driven personalization; increase the efficiency and convenience of customer service; and gain deep insights into customer needs through data analysis and predictive analytics to offer new products and services.

MCL317 – Orchestrating Machine Learning Training for Netflix Recommendations
At Netflix, we use machine learning (ML) algorithms extensively to recommend relevant titles to our 100+ million members based on their tastes. Everything on the member home page is an evidence-driven, A/B-tested experience that we roll out backed by ML models. These models are trained using Meson, our workflow orchestration system. Meson distinguishes itself from other workflow engines by handling more sophisticated execution graphs, such as loops and parameterized fan-outs. Meson can schedule Spark jobs, Docker containers, bash scripts, gists of Scala code, and more. Meson also provides a rich visual interface for monitoring active workflows and inspecting execution logs. It has a powerful Scala DSL for authoring workflows as well as the REST API. In this session, we focus on how Meson trains recommendation ML models in production, and how we have re-architected it to scale up for a growing need of broad ETL applications within Netflix. As a driver for this change, we have had to evolve the persistence layer for Meson. We talk about how we migrated from Cassandra to Amazon RDS backed by Amazon Aurora

MCL350 – Humans vs. the Machines: How Pinterest Uses Amazon Mechanical Turk’s Worker Community to Improve Machine Learning
Ever since the term “crowdsourcing” was coined in 2006, it’s been a buzzword for technology companies and social institutions. In the technology sector, crowdsourcing is instrumental for verifying machine learning algorithms, which, in turn, improves the user’s experience. In this session, we explore how Pinterest adapted to an increased reliability on human evaluation to improve their product, with a focus on how they’ve integrated with Mechanical Turk’s platform. This presentation is aimed at engineers, analysts, program managers, and product managers who are interested in how companies rely on Mechanical Turk’s human evaluation platform to better understand content and improve machine learning algorithms. The discussion focuses on the analysis and product decisions related to building a high quality crowdsourcing system that takes advantage of Mechanical Turk’s powerful worker community.

Service sessions: architecture and best practices

Featuring Amazon EMR, Amazon Athena, Amazon Kinesis, AWS Glue, Amazon QuickSight, Amazon Redshift, Amazon Elasticsearch Service, and Amazon DynamoDB.

ABD201 – Big Data Architectural Patterns and Best Practices on AWS
In this session, we simplify big data processing as a data bus comprising various stages: collect, store, process, analyze, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost

ABD202 – Best Practices for Building Serverless Big Data Applications
Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. In this session, we show you how to incorporate serverless concepts into your big data architectures. We explore the concepts behind and benefits of serverless architectures for big data, looking at design patterns to ingest, store, process, and visualize your data. Along the way, we explain when and how you can use serverless technologies to streamline data processing, minimize infrastructure management, and improve agility and robustness and share a reference architecture using a combination of cloud and open source technologies to solve your big data problems. Topics include: use cases and best practices for serverless big data applications; leveraging AWS technologies such as Amazon DynamoDB, Amazon S3, Amazon Kinesis, AWS Lambda, Amazon Athena, and Amazon EMR; and serverless ETL, event processing, ad hoc analysis, and real-time analytics.

ABD206 – Building Visualizations and Dashboards with Amazon QuickSight
Just as a picture is worth a thousand words, a visual is worth a thousand data points. A key aspect of our ability to gain insights from our data is to look for patterns, and these patterns are often not evident when we simply look at data in tables. The right visualization will help you gain a deeper understanding in a much quicker timeframe. In this session, we will show you how to quickly and easily visualize your data using Amazon QuickSight. We will show you how you can connect to data sources, generate custom metrics and calculations, create comprehensive business dashboards with various chart types, and setup filters and drill downs to slice and dice the data.

ABD203 – Real-Time Streaming Applications on AWS: Use Cases and Patterns
To win in the marketplace and provide differentiated customer experiences, businesses need to be able to use live data in real time to facilitate fast decision making. In this session, you learn common streaming data processing use cases and architectures. First, we give an overview of streaming data and AWS streaming data capabilities. Next, we look at a few customer examples and their real-time streaming applications. Finally, we walk through common architectures and design patterns of top streaming data use cases.

ABD213 – How to Build a Data Lake with AWS Glue Data Catalog
As data volumes grow and customers store more data on AWS, they often have valuable data that is not easily discoverable and available for analytics. The AWS Glue Data Catalog provides a central view of your data lake, making data readily available for analytics. We introduce key features of the AWS Glue Data Catalog and its use cases. Learn how crawlers can automatically discover your data, extract relevant metadata, and add it as table definitions to the AWS Glue Data Catalog. We will also explore the integration between AWS Glue Data Catalog and Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.

ABD214 – Real-time User Insights for Mobile and Web Applications with Amazon Pinpoint
With customers demanding relevant and real-time experiences across a range of devices, digital businesses are looking to gather user data at scale, understand this data, and respond to customer needs instantly. This requires tools that can record large volumes of user data in a structured fashion, and then instantly make this data available to generate insights. In this session, we demonstrate how you can use Amazon Pinpoint to capture user data in a structured yet flexible manner. Further, we demonstrate how this data can be set up for instant consumption using services like Amazon Kinesis Firehose and Amazon Redshift. We walk through example data based on real world scenarios, to illustrate how Amazon Pinpoint lets you easily organize millions of events, record them in real-time, and store them for further analysis.

ABD223 – IT Innovators: New Technology for Leveraging Data to Enable Agility, Innovation, and Business Optimization
Companies of all sizes are looking for technology to efficiently leverage data and their existing IT investments to stay competitive and understand where to find new growth. Regardless of where companies are in their data-driven journey, they face greater demands for information by customers, prospects, partners, vendors and employees. All stakeholders inside and outside the organization want information on-demand or in “real time”, available anywhere on any device. They want to use it to optimize business outcomes without having to rely on complex software tools or human gatekeepers to relevant information. Learn how IT innovators at companies such as MasterCard, Jefferson Health, and TELUS are using Domo’s Business Cloud to help their organizations more effectively leverage data at scale.

ABD301 – Analyzing Streaming Data in Real Time with Amazon Kinesis
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we present an end-to-end streaming data solution using Kinesis Streams for data ingestion, Kinesis Analytics for real-time processing, and Kinesis Firehose for persistence. We review in detail how to write SQL queries using streaming data and discuss best practices to optimize and monitor your Kinesis Analytics applications. Lastly, we discuss how to estimate the cost of the entire system

ABD302 – Real-Time Data Exploration and Analytics with Amazon Elasticsearch Service and Kibana
In this session, we use Apache web logs as example and show you how to build an end-to-end analytics solution. First, we cover how to configure an Amazon ES cluster and ingest data using Amazon Kinesis Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data. Then we demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we review approaches for generating custom, ad-hoc reports.

ABD304 – Best Practices for Data Warehousing with Amazon Redshift & Redshift Spectrum
Most companies are over-run with data, yet they lack critical insights to make timely and accurate business decisions. They are missing the opportunity to combine large amounts of new, unstructured big data that resides outside their data warehouse with trusted, structured data inside their data warehouse. In this session, we take an in-depth look at how modern data warehousing blends and analyzes all your data, inside and outside your data warehouse without moving the data, to give you deeper insights to run your business. We will cover best practices on how to design optimal schemas, load data efficiently, and optimize your queries to deliver high throughput and performance.

ABD305 – Design Patterns and Best Practices for Data Analytics with Amazon EMR
Amazon EMR is one of the largest Hadoop operators in the world, enabling customers to run ETL, machine learning, real-time processing, data science, and low-latency SQL at petabyte scale. In this session, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters, and other Amazon EMR architectural best practices. We talk about lowering cost with Auto Scaling and Spot Instances, and security best practices for encryption and fine-grained access control. Finally, we dive into some of our recent launches to keep you current on our latest features.

ABD307 – Deep Analytics for Global AWS Marketing Organization
To meet the needs of the global marketing organization, the AWS marketing analytics team built a scalable platform that allows the data science team to deliver custom econometric and machine learning models for end user self-service. To meet data security standards, we use end-to-end data encryption and different AWS services such as Amazon Redshift, Amazon RDS, Amazon S3, Amazon EMR with Apache Spark and Auto Scaling. In this session, you see real examples of how we have scaled and automated critical analysis, such as calculating the impact of marketing programs like re:Invent and prioritizing leads for our sales teams.

ABD311 – Deploying Business Analytics at Enterprise Scale with Amazon QuickSight
One of the biggest tradeoffs customers usually make when deploying BI solutions at scale is agility versus governance. Large-scale BI implementations with the right governance structure can take months to design and deploy. In this session, learn how you can avoid making this tradeoff using Amazon QuickSight. Learn how to easily deploy Amazon QuickSight to thousands of users using Active Directory and Federated SSO, while securely accessing your data sources in Amazon VPCs or on-premises. We also cover how to control access to your datasets, implement row-level security, create scheduled email reports, and audit access to your data.

ABD315 – Building Serverless ETL Pipelines with AWS Glue
Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), APIs, clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Additionally, Merck will share how they built an end-to-end ETL pipeline for their application release management system, and launched it in production in less than a week using AWS Glue.

ABD318 – Architecting a data lake with Amazon S3, Amazon Kinesis, and Amazon Athena
Learn how to architect a data lake where different teams within your organization can publish and consume data in a self-service manner. As organizations aim to become more data-driven, data engineering teams have to build architectures that can cater to the needs of diverse users – from developers, to business analysts, to data scientists. Each of these user groups employs different tools, have different data needs and access data in different ways. In this talk, we will dive deep into assembling a data lake using Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, and AWS Glue. The session will feature Mohit Rao, Architect and Integration lead at Atlassian, the maker of products such as JIRA, Confluence, and Stride. First, we will look at a couple of common architectures for building a data lake. Then we will show how Atlassian built a self-service data lake, where any team within the company can publish a dataset to be consumed by a broad set of users.

ABD 323 Extending Analytics Beyond the Data warehouse

Companies have valuable data that they may not be analyzing due to the complexity, scalability, and performance issues of loading the data into their data warehouse. However, with the right tools, you can extend your analytics to query data in your data lake—with no loading required. Amazon Redshift Spectrum extends the analytic power of Amazon Redshift beyond data stored in your data warehouse to run SQL queries directly against vast amounts of unstructured data in your Amazon S3 data lake. This gives you the freedom to store your data where you want, in the format you want, and have it available for analytics when you need it. Join a discussion with AWS solution architects to ask question.

ABD330 – Combining Batch and Stream Processing to Get the Best of Both Worlds
Today, many architects and developers are looking to build solutions that integrate batch and real-time data processing, and deliver the best of both approaches. Lambda architecture (not to be confused with the AWS Lambda service) is a design pattern that leverages both batch and real-time processing within a single solution to meet the latency, accuracy, and throughput requirements of big data use cases. Come join us for a discussion on how to implement Lambda architecture (batch, speed, and serving layers) and best practices for data processing, loading, and performance tuning

ABD335 – Real-Time Anomaly Detection Using Amazon Kinesis
Amazon Kinesis Analytics offers a built-in machine learning algorithm that you can use to easily detect anomalies in your VPC network traffic and improve security monitoring. Join us for an interactive discussion on how to stream your VPC flow Logs to Amazon Kinesis Streams and identify anomalies using Kinesis Analytics.

ABD339 – Deep Dive and Best Practices for Amazon Athena
Amazon Athena is an interactive query service that enables you to process data directly from Amazon S3 without the need for infrastructure. Since its launch at re:invent 2016, several organizations have adopted Athena as the central tool to process all their data. In this talk, we dive deep into the most common use cases, including working with other AWS services. We review the best practices for creating tables and partitions and performance optimizations. We also dive into how Athena handles security, authorization, and authentication. Lastly, we hear from a customer who has reduced costs and improved time to market by deploying Athena across their organization.

We look forward to meeting you at re:Invent 2017!

About the Author

Roy Ben-Alta is a solution architect and principal business development manager at Amazon Web Services in New York. He focuses on Data Analytics and ML Technologies, working with AWS customers to build innovative data-driven products.

Kim Dotcom Asks Court For $829K to Fund Family Expenses

2017-11-02 Andy

Post Syndicated from Andy original https://torrentfreak.com/kim-dotcom-asks-court-for-829k-to-fund-family-expenses-171102/

When Megaupload was shut down in January 2012, US authorities did everything within their power to financially ruin Kim Dotcom and his associates.

Aside from taking much of his personal property, tens of millions of dollars of assets were seized around the world.

Under instruction from the U.S. government, US$42.57m in assets were seized in Hong Kong and since then Dotcom has been trying to claw it back, bit by bit.

Back in July, Dotcom revealed that the Hong Kong High Court had released more of his funds, plus four containers of seized property. Noting that he missed the country, he gave thanks for the lifeline.

“Thanks to a Hong Kong Judge my family can move to Queenstown and my kids will be surrounded by beautiful mountains & lakes instead of spies,” Dotcom said.

That move eventually went ahead, with Dotcom regularly tweeting beautiful waterside views from his new home over the past few months. But of course, nice things tend to cost quite a bit of money, so Dotcom’s legal team have been working hard in Hong Kong to have more funds released.

According to a report from NZHerald, his latest request is fairly sizeable, reaching NZ$1.2m (US$829,400), everything considered.

First up, Dotcom is seeking around NZ$1m (US$691,200) for costs relating to his relocation from Auckland to Queenstown. That’s comprised of two years worth of rent at NZ$40,000 (US$27,648) per month (no typo), plus NZ$150,000 (US$103,680) to cover the actual cost of the move.

On top, Dotcom is looking for NZ$73,000 (US$50,457) per month for living expenses, an amount that’s roughly US$2,000 per month up on the amount he currently receives.

According to the report, Dotcom’s team are also proposing a further amount of NZ$200,000 (US$138,240) to cover emergency items including “medical expenses of the family, car maintenance, household repairs and two holidays of the family”.

It seems unlikely that this will be the final request from Dotcom. According to Gerard McCoy, Dotcom’s lawyer in Hong Kong, the extradition process in New Zealand is nowhere near complete. In fact, McCoy told the court that proceedings won’t be completed during the next two years.

That takes us to 2020, at least, meaning that Dotcom will still be in New Zealand a full eight years after the raid. Given the massive number of court battles and subsequent appeals into every detail of several resulting cases, that’s probably not a surprise, however.

The progress in the extradition process itself is also somewhat glacial, with the next hearing set for the first quarter of 2018 in the Court of Appeal. If past experience is anything to go by, neither side will be happy with the outcome. This means that an appeal to the Supreme Court is almost inevitable.

Over in the United States, progress has also been slow. Recently, a petition from Dotcom and his former Megaupload colleagues over millions of dollars in seized assets was denied by the US Supreme Court.

While this decision means that the battle over a further US$67 million in assets has been exhausted, the question of whether Dotcom and former colleagues Mathias Ortmann, Bram van der Kolk and Finn Batato will ever stand trial in the US remains unanswered.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Hot Startups on AWS – October 2017

2017-10-31 Tina Barr

Post Syndicated from Tina Barr original https://aws.amazon.com/blogs/aws/hot-startups-on-aws-october-2017/

In 2015, the Centers for Medicare and Medicaid Services (CMS) reported that healthcare spending made up 17.8% of the U.S. GDP – that’s almost $3.2 trillion or $9,990 per person. By 2025, the CMS estimates this number will increase to nearly 20%. As cloud technology evolves in the healthcare and life science industries, we are seeing how companies of all sizes are using AWS to provide powerful and innovative solutions to customers across the globe. This month we are excited to feature the following startups:

ClearCare – helping home care agencies operate efficiently and grow their business.
DNAnexus – providing a cloud-based global network for sharing and managing genomic data.

ClearCare (San Francisco, CA)

ClearCare envisions a future where home care is the only choice for aging in place. Home care agencies play a critical role in the economy and their communities by significantly lowering the overall cost of care, reducing the number of hospital admissions, and bending the cost curve of aging. Patients receiving home care typically have multiple chronic conditions and functional limitations, driving over $190 billion in healthcare spending in the U.S. each year. To offset these costs, health insurance payers are developing in-home care management programs for patients. ClearCare’s goal is to help home care agencies leverage technology to improve costs, outcomes, and quality of life for the aging population. The company’s powerful software platform is specifically designed for use by non-medical, in-home care agencies to manage their businesses.

Founder and CEO Geoff Nudd created ClearCare because of his own grandmother’s need for care. Keeping family members and caregivers up to date on a loved one’s well being can be difficult, so Geoff created what is now ClearCare’s Family Room, which enables caregivers and agency staff to check schedules and receive real-time updates about what’s happening in the home. Since then, agencies have provided feedback on others areas of their businesses that could be streamlined. ClearCare has now built over 20 modules to help home care agencies optimize operations with services including a telephony service, billing and payroll, and more. ClearCare now serves over 4,000 home care agencies, representing 500,000 caregivers and 400,000 seniors.

Using AWS, ClearCare is able to spin up reliable infrastructure for proofs of concept and iterate on those systems to quickly get value to market. The company runs many AWS services including Amazon Elasticsearch Service, Amazon RDS, and Amazon CloudFront. Amazon EMR and Amazon Athena have enabled ClearCare to build a Hadoop-based ETL and data warehousing system that processes terabytes of data each day. By utilizing these managed services, ClearCare has been able to go from concept to customer delivery in less than three months.

To learn more about ClearCare, check out their website.

DNAnexus (Mountain View, CA)

DNAnexus is accelerating the application of genomic data in precision medicine by providing a cloud-based platform for sharing and managing genomic and biomedical data and analysis tools. The company was founded in 2009 by Stanford graduate student Andreas Sundquist and two Stanford professors Arend Sidow and Serafim Batzoglou, to address the need for scaling secondary analysis of next-generation sequencing (NGS) data in the cloud. The founders quickly learned that users needed a flexible solution to build complex analysis workflows and tools that enable them to share and manage large volumes of data. DNAnexus is optimized to address the challenges of security, scalability, and collaboration for organizations that are pursuing genomic-based approaches to health, both in clinics and research labs. DNAnexus has a global customer base – spanning North America, Europe, Asia-Pacific, South America, and Africa – that runs a million jobs each month and is doubling their storage year-over-year. The company currently stores more than 10 petabytes of biomedical and genomic data. That is equivalent to approximately 100,000 genomes, or in simpler terms, over 50 billion Facebook photos!

DNAnexus is working with its customers to help expand their translational informatics research, which includes expanding into clinical trial genomic services. This will help companies developing different medicines to better stratify clinical trial populations and develop companion tests that enable the right patient to get the right medicine. In collaboration with Janssen Human Microbiome Institute, DNAnexus is also launching Mosaic – a community platform for microbiome research.

AWS provides DNAnexus and its customers the flexibility to grow and scale research programs. Building the technology infrastructure required to manage these projects in-house is expensive and time-consuming. DNAnexus removes that barrier for labs of any size by using AWS scalable cloud resources. The company deploys its customers’ genomic pipelines on Amazon EC2, using Amazon S3 for high-performance, high-durability storage, and Amazon Glacier for low-cost data archiving. DNAnexus is also an AWS Life Sciences Competency Partner.

Learn more about DNAnexus here.

—

-Tina

PureVPN Explains How it Helped the FBI Catch a Cyberstalker

2017-10-16 Andy

Post Syndicated from Andy original https://torrentfreak.com/purevpn-explains-how-it-helped-the-fbi-catch-a-cyberstalker-171016/

Early October, Ryan S. Lin, 24, of Newton, Massachusetts, was arrested on suspicion of conducting “an extensive cyberstalking campaign” against a 24-year-old Massachusetts woman, as well as her family members and friends.

The Department of Justice described Lin’s offenses as a “multi-faceted” computer hacking and cyberstalking campaign. Launched in April 2016 when he began hacking into the victim’s online accounts, Lin allegedly obtained personal photographs and sensitive information about her medical and sexual histories and distributed that information to hundreds of other people.

Details of what information the FBI compiled on Lin can be found in our earlier report but aside from his alleged crimes (which are both significant and repugnant), it was PureVPN’s involvement in the case that caused the most controversy.

In a report compiled by an FBI special agent, it was revealed that the Hong Kong-based company’s logs helped the authorities net the alleged criminal.

“Significantly, PureVPN was able to determine that their service was accessed by the same customer from two originating IP addresses: the RCN IP address from the home Lin was living in at the time, and the software company where Lin was employed at the time,” the agent’s affidavit reads.

Among many in the privacy community, this revelation was met with disappointment. On the PureVPN website the company claims to carry no logs and on a general basis, it’s expected that so-called “no-logging” VPN providers should provide people with some anonymity, at least as far as their service goes. Now, several days after the furor, the company has responded to its critics.

In a fairly lengthy statement, the company begins by confirming that it definitely doesn’t log what websites a user views or what content he or she downloads.

“PureVPN did not breach its Privacy Policy and certainly did not breach your trust. NO browsing logs, browsing habits or anything else was, or ever will be shared,” the company writes.

However, that’s only half the problem. While it doesn’t log user activity (what sites people visit or content they download), it does log the IP addresses that customers use to access the PureVPN service. These, given the right circumstances, can be matched to external activities thanks to logs carried by other web companies.

PureVPN talks about logs held by Google’s Gmail service to illustrate its point.

“A network log is automatically generated every time a user visits a website. For the sake of this example, let’s say a user logged into their Gmail account. Every time they accessed Gmail, the email provider created a network log,” the company explains.

“If you are using a VPN, Gmail’s network log would contain the IP provided by PureVPN. This is one half of the picture. Now, if someone asks Google who accessed the user’s account, Google would state that whoever was using this IP, accessed the account.

“If the user was connected to PureVPN, it would be a PureVPN IP. The inquirer [in the Lin case, the FBI] would then share timestamps and network logs acquired from Google and ask them to be compared with the network logs maintained by the VPN provider.”

Now, if PureVPN carried no logs – literally no logs – it would not be able to help with this kind of inquiry. That was the case last year when the FBI approached Private Internet Access for information and the company was unable to assist.

However, as is made pretty clear by PureVPN’s explanation, the company does log user IP addresses and timestamps which reveal when a user was logged on to the service. It doesn’t matter that PureVPN doesn’t log what the user allegedly did online, since the third-party service already knows that information to the precise second.

Following the example, GMail knows that a user sent an email at 10:22am on Monday October 16 from a PureVPN IP address. So, if PureVPN is approached by the FBI, the company can confirm that User X was using the same IP address at exactly the same time, and his home IP address was XXX.XX.XXX.XX. Effectively, the combined logs link one IP address to the other and the user is revealed. It’s that simple.

It is for this reason that in TorrentFreak’s annual summary of no-logging VPN providers, the very first question we ask every single company reads as follows:

Do you keep ANY logs which would allow you to match an IP-address and a time stamp to a user/users of your service? If so, what information do you hold and for how long?

Clearly, if a company says “yes we log incoming IP addresses and associated timestamps”, any claim to total user anonymity is ended right there and then.

While not completely useless (a logging service will still stop the prying eyes of ISPs and similar surveillance, while also defeating throttling and site-blocking), if you’re a whistle-blower with a job or even your life to protect, this level of protection is entirely inadequate.

The take-home points from this controversy are numerous, but perhaps the most important is for people to read and understand VPN provider logging policies.

Secondly, and just as importantly, VPN providers need to be extremely clear about the information they log. Not tracking browsing or downloading activities is all well and good, but if home IP addresses and timestamps are stored, this needs to be made clear to the customer.

Finally, VPN users should not be evil. There are plenty of good reasons to stay anonymous online but cyberstalking, death threats and ruining people’s lives are not included. Fortunately, the FBI have offline methods for catching this type of offender, and long may that continue.

PureVPN’s blog post is available here.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

PureVPN Logs Helped FBI Net Alleged Cyberstalker

2017-10-09 Andy

Post Syndicated from Andy original https://torrentfreak.com/purevpn-logs-helped-fbi-net-alleged-cyberstalker-171009/

Last Thursday, Ryan S. Lin, 24, of Newton, Massachusetts, was arrested on suspicion of conducting “an extensive cyberstalking campaign” against his former roommate, a 24-year-old Massachusetts woman, as well as her family members and friends.

According to the Department of Justice, Lin’s “multi-faceted campaign of computer hacking and cyberstalking” began in April 2016 when he began hacking into the victim’s online accounts, obtaining personal photographs, sensitive information about her medical and sexual histories, and other private details.

It’s alleged that after obtaining the above material, Lin distributed it to hundreds of others. It’s claimed he created fake online profiles showing the victim’s home address while soliciting sexual activity. This caused men to show up at her home.

“Mr. Lin allegedly carried out a relentless cyber stalking campaign against a young woman in a chilling effort to violate her privacy and threaten those around her,” said Acting United States Attorney William D. Weinreb.

“While using anonymizing services and other online tools to avoid attribution, Mr. Lin harassed the victim, her family, friends, co-workers and roommates, and then targeted local schools and institutions in her community. Mr. Lin will now face the consequences of his crimes.”

While Lin awaits his ultimate fate (he appeared in U.S. District Court in Boston Friday), the allegation he used anonymization tools to hide himself online but still managed to get caught raises a number of questions. An affidavit submitted by Special Agent Jeffrey Williams in support of the criminal complaint against Lin provides most of the answers.

Describing Lin’s actions against the victim as “doxing”, Williams begins by noting that while Lin was the initial aggressor, the fact he made the information so widely available raises the possibility that other people got involved with malicious acts later on. Nevertheless, Lin remains the investigation’s prime suspect.

According to the affidavit, Lin is computer savvy having majored in computer science. He allegedly utilized a number of methods to hide his identity and IP address, including TOR, Virtual Private Network (VPN) services and email providers that “do not maintain logs or other records.”

But if that genuinely is the case, how was Lin caught?

First up, it’s worth noting that plenty of Lin’s aggressive and stalking behaviors towards the victim were demonstrated in a physical sense, offline. In that respect, it appears the authorities already had him as the prime suspect and worked back from there.

In one instance, the FBI examined a computer that had been used by Lin at a former workplace. Although Windows had been reinstalled, the FBI managed to find Google Chrome data which indicated Lin had viewed articles about bomb threats he allegedly made. They were also able to determine he’d accessed the victim’s Gmail account and additional data suggested that he’d used a VPN service.

“Artifacts indicated that PureVPN, a VPN service that was used repeatedly in the cyberstalking scheme, was installed on the computer,” the affidavit reads.

From here the Special Agent’s report reveals that the FBI received cooperation from Hong Kong-based PureVPN.

Needless to say, while this information will prove useful to the FBI’s prosecution of Lin, it’s also likely to turn into a huge headache for the VPN provider. The company claims zero-logging, which clearly isn’t the case.

“PureVPN operates a self-managed VPN network that currently stands at 750+ Servers in 141 Countries. But is this enough to ensure complete security?” the company’s marketing statement reads.

“That’s why PureVPN has launched advanced features to add proactive, preventive and complete security. There are no third-parties involved and NO logs of your activities.”

PureVPN privacy graphic

However, if one drills down into the PureVPN privacy policy proper, one sees the following:

Our servers automatically record the time at which you connect to any of our servers. From here on forward, we do not keep any records of anything that could associate any specific activity to a specific user. The time when a successful connection is made with our servers is counted as a ‘connection’ and the total bandwidth used during this connection is called ‘bandwidth’. Connection and bandwidth are kept in record to maintain the quality of our service. This helps us understand the flow of traffic to specific servers so we could optimize them better.

This seems to match what the FBI says – almost. While it says it doesn’t log, PureVPN admits to keeping records of when a user connects to the service and for how long. The FBI clearly states that the service also captures the user’s IP address too. In fact, it appears that PureVPN also logged the IP address belonging to another VPN service (WANSecurity) that was allegedly used by Lin to connect to PureVPN.

That record also helped to complete another circle of evidence. IP addresses used by
Kansas-based WANSecurity and Secure Internet LLC (servers operated by PureVPN) were allegedly used to access Gmail accounts known to be under Lin’s control.

Somewhat ironically, this summer Lin took to Twitter to criticize VPN provider IPVanish (which is not involved in the case) over its no-logging claims.

“There is no such thing as a VPN that doesn’t keep logs,” Lin said. “If they can limit your connections or track bandwidth usage, they keep logs.”

Or, in the case of PureVPN, if they log a connection time and a source IP address, that could be enough to raise the suspicions of the FBI and boost what already appears to be a pretty strong case.

If convicted, Lin faces up to five years in prison and three years of supervised release.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Building a Real World Evidence Platform on AWS

2017-08-09 Aaron Friedman

Post Syndicated from Aaron Friedman original https://aws.amazon.com/blogs/big-data/building-a-real-world-evidence-platform-on-aws/

Deriving insights from large datasets is central to nearly every industry, and life sciences is no exception. To combat the rising cost of bringing drugs to market, pharmaceutical companies are looking for ways to optimize their drug development processes. They are turning to big data analytics to better quantify the effect that their drug compounds have on different populations and to look for new clinical indications for existing drugs.

A real world evidence (RWE) platform is loosely defined as an integrated set of services and products that life sciences companies use to securely acquire, store, and analyze large, often disparate datasets to gain insight into the functions of a specific drug or intervention. The petabyte-scale data generated from wearables, medical devices, genomics, clinical imaging, and claims (to name a few) allows pharmaceutical and other life sciences companies to build big data platforms to analyze these datasets. And many of them are doing it on AWS.

At the center of nearly every RWE platform is a data lake that houses different data types. It also stores the related metadata to identify where each piece of data came from, who owned it, etc. Analytics engines integrate the relevant streaming (e.g., wearables), structured (e.g., claims data), and unstructured (e.g., notes in electronic health records) data.

In this post, I highlight common architectural patterns that customers are using to maximize the value of real world evidence on AWS. The architecture presented here can be reproduced in multiple regions, so you can respect local data sovereignty requirements, when applicable, while conducting global studies. This post doesn’t cover all considerations for real world evidence (such as security and authentication), but instead focuses on the areas that are related to your data flow.

A data lake is your source of truth

The ability to integrate disparate data types is critical to maximizing the utility of RWE. Life sciences companies have to be able to store, search, and retrieve data of different types and sizes, including (but not limited to) the following:

Streaming data from wearables and medical devices
Structured data from genomics and claims data
Unstructured data from notes in electronic health records

A common solution to integrate these data types is a data lake. Data lakes allow organizations to store all their data, regardless of data type, in a centralized repository. Because data can be stored as-is, there’s no need to convert it to a predefined schema. And you no longer need to know what questions you want to ask of your data beforehand. You can use data lakes for ad hoc analyses, so you can quickly explore and discover new insights without needing to structure the data first, as you would with a traditional data warehouse.

Although there are many uses for storing real world evidence data in a data lake, here are a few examples of the processes it can facilitate for pharma and biotech companies:

Quickly access all data for a given subject, from clinical images to genomics to claims data.
Associate incoming RWE data with existing data in your RWE data lake, such as by subject or study.
Select specific windows in longitudinal studies (microbiome, metabolomics, etc.).

The following diagram shows an architecture of the features within a data lake and several examples of how data enters a data lake:

This architecture, which is entirely serverless and backed by Amazon S3, lets you scale your data lake to easily accommodate any data size for your RWE platform. Additionally, the components presented in this diagram can be secured via IAM controls and service policies. This enables you to secure and protect the sensitive information that often resides within an RWE platform. Amazon S3 is the canonical source for objects in your RWE data lake. You track and search metadata that is associated with these objects in a data catalog built on Amazon Elasticsearch Service and Amazon DynamoDB.

Let’s dive deeper into what this architecture does and how data makes it into the data lake:

Streaming (e.g., wearables), structured, and unstructured data is acquired from myriad devices and sources. Depending on the data size, you might use AWS IoT (streaming), AWS Storage Gateway (mid-size/continual batch), and AWS Snowball (large legacy datasets, such as imaging). AWS IoT writes to Amazon Kinesis Firehose, which transforms the telemetry data in-flight to land both transformed and raw data in Amazon S3.
When data lands in Amazon S3 buckets, an AWS Lambda function is invoked (either by trigger or manually).
This Lambda function writes to a data catalog that is fronted by Amazon API Gateway. The data catalog contains metadata about all the object data in Amazon S3, as well as data that resides in databases, such as Amazon Redshift.
An AWS Lambda function on the other side of API Gateway writes the appropriate metadata about the objects, such as the study that the data was generated from, into Amazon Elasticsearch Service and/or Amazon DynamoDB, which I refer to as the data catalog.

The data catalog mentioned in steps 3 and 4 is central to your data management. In most analyses, the first step is to build a data manifest of where the data-of-interest lies, which is discussed in later sections.

Normalizing data for real world evidence

As I previously mentioned, data can enter your real world evidence data lake in many different formats. In many cases, you might need to normalize this data into a specific format to ease downstream analysis. For example, you might have to integrate many different electronic health records that are stored in different formats. You also might have to transform genomics data, often represented as a variant call format (VCF) file, into a format that’s easier for big data technologies like Apache Parquet to query. While you can certainly analyze this data in the format in which it entered, it might be better for querying after it’s transformed into a different format, or into a different data store.

In the architecture shown in the following diagram, AWS Step Functions orchestrates the data normalization (extract, transform, load—or ETL) process. Step Functions is a serverless workflow service that ensures that your long-running ETL jobs execute in order and complete successfully. At a high level, you first query the data catalog to get a manifest of the data to be normalized. Normalization occurs on Amazon EC2 instances, and the results are then stored back in Amazon S3 or in databases such as Amazon Redshift for future analysis. Locations of these results data are also logged in the data catalog for future querying.

Here is an example Step Functions state machine for this process:

The following is the accompanying JSON that produces it:

{
  "Comment": "An example for data normalization in RWE",
  "StartAt": "GenerateDataManifest",
  "States": {
    "GenerateDataManifest": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>::function:GenerateDataManifest",
      "Retry": [ {
        "ErrorEquals": ["States.Timeout"],
        "MaxAttempts": 2
      } ],
      "Next": "SubmitNormalizationJob"
    },
    "SubmitNormalizationJob": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>::function: SubmitNormalizationJob",
      "Next": "GetNormalizationJobStatus"
    },
    "GetNormalizationJobStatus": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>:function: GetNormalizationJobStatus",
      "Next": "CheckJobStatus",
      "InputPath": "$",
      "ResultPath": "$.status"
    },
    "CheckJobStatus": {
      "Type": "Choice",
      "Choices": [
        {
          "Variable": "$.status",
          "StringEquals": "FAILED",
          "Next": "JobFailureCleanup"
        },
        {
          "Variable": "$.status",
          "StringEquals": "SUCCEEDED",
          "Next": "JobSuccessCleanup"
        }
      ],
      "Default": "Wait60Seconds"
    },
    "Wait60Seconds": {
      "Type": "Wait",
      "Seconds": 60,
      "Next": "GetNormalizationJobStatus"
    },
    "JobSuccessCleanup": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>:function:batchJobSuccessCleanup",
      "End": true
    },
    "JobFailureCleanup": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>:function:batchJobSuccessCleanup",
      "End": true
    }
  }
}

And here is the overall architecture:

Here are the steps in more detail:

Either a user or an application submits a POST request through Amazon API Gateway to process or transform a set of data. This request contains the query parameters that correspond to generating the data manifest. It is passed into an AWS Step Functions state machine, which orchestrates the ETL process.
A Lambda function queries the data catalog and builds a manifest that contains the location of the data of interest.
The manifest is passed to downstream Lambda functions in the state machine that orchestrate the batch workflow to process the data that’s specified in the manifest.
These Lambda functions submit jobs to AWS Batch to execute batch jobs on Amazon EC2.
Amazon EC2 processes the data (e.g., data normalization) and stores results back in Amazon S3 and/or Amazon Redshift.
Results data is then logged in the data catalog, using the process shown in the data acquisition diagram.

Analyzing and visualizing data in real world evidence

Ultimately, the value in real world evidence platforms is the value you derive from the wide variety of data that feeds into it. Big data analytics, such as Spark on EMR, machine learning with the Deep Learning AMIs, and healthcare data warehouses, are all fundamental to maximizing the value of RWE.

Given the wide array of questions you can answer, you want your architecture to be as flexible as possible. Your organization also might use business intelligence (BI) tools. Again, as with the data normalization tier, you can use Step Functions to orchestrate your workflow. You first build the data manifest and then submit each portion of your desired analysis to the relevant data location and compute options, such as Amazon EMR or Amazon Redshift. Your organization’s BI tool of choice, such as Amazon QuickSight or one offered by our AWS Big Data Competency partners, can extract and visualize the results.

Here is the workflow in more detail:

A user initiates a query on a dataset. In the context of RWE, this is largely analyzing a cohort in the context of a specific indication (drug response, etc.).
AWS Step Functions invokes a Lambda function to query the data catalog and build a manifest of data.
The manifest is passed to subsequent Lambda functions, which orchestrate the data analysis through different AWS services.
These analyses can include Amazon EMR for population-scale genomics, Amazon EC2 for HPC and machine learning, and Amazon Redshift for your healthcare data warehouse.
Results from each analysis are staged back in Amazon S3 and logged in the data catalog.
BI tools like Amazon QuickSight or Tableau, or data analysis workbooks like Jupyter can query Amazon S3 to visualize results, such as through Amazon Athena.

A practical example

Imagine that you are at a pharmaceutical company that is developing a drug to address a neurological condition. You have longitudinal data in the form of brain scans and have noticed that the drug compound under development seems to benefit a specific subpopulation of individuals. As a result, you determine to sequence the genomes of all the responders and non-responders to look for specific biomarkers that could indicate response levels. Success would result in a companion diagnostic that you could use alongside your drug to increase its overall efficacy by delivering it only to the responding population.

Let’s briefly look at how this study might play out in the steps of data acquisition, normalization, and analysis described earlier.

Data acquisition

Data from longitudinal brain imaging studies can be moved to AWS using AWS Snowball. Genomics data coming off your genome sequencers would land in Amazon S3 via AWS Storage Gateway and/or AWS Direct Connect. These datasets are stored in your data catalog where you track items such as date generated, anonymized subject ID, etc.

Data processing

Genomes are transformed from raw reads (e.g., FASTQ) to a human readable format (VCF) that identifies variations in a genome. These VCF files are subsequently extracted, transformed, and loaded into a format that is amenable to big data analytics, such as Parquet.

Data analysis

You build a data manifest of your brain scans in Amazon S3. You use the deep learning AMI and P2 instance family to build machine learning models to identify images that represent different stages in your disorder-of-interest. You then run association analyses that combine your genomics data with your brain imaging models and drug response data to identify what genomic variants associate with improved treatment outcomes. You can manage these analyses via Jupyter notebooks, or you can connect with your BI tool of choice to visualize your results.

It will always be day one for RWE

By implementing real world evidence platforms on AWS, you can quickly integrate and interrogate disparate healthcare data to advance human health. By designing your RWE platform to be flexible, you can readily incorporate new big data technologies as they become relevant to your needs. This enables you to innovate and discover at a quicker rate.

To read more about real world evidence on AWS, and how AWS Partner Network (APN) Premier Consulting Partner Deloitte has architected their ConvergeHEALTH platform on AWS, check out this guest post on the APN Blog!

Please leave any questions and comments below.

Additional Reading

The healthcare ecosystem has chosen a variety of tools and techniques for working with big data, but one tool that comes up again and again in many of the architectures we design and review is Spark on Amazon EMR. Will spark power the data behind precision medicine?

About the Authors

Dr. Aaron Friedman is a Healthcare and Life Sciences Partner Solutions Architect at Amazon Web Services. He works with ISVs and SIs to architect healthcare solutions on AWS, and bring the best possible experience to their customers. His passion is working at the intersection of science, big data, and software. In his spare time, he’s exploring the outdoors, learning a new thing to cook, or spending time with his wife and his dog, Macaroon.

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