Tag Archives: media

postmarketOS Low-Level

Post Syndicated from ris original https://lwn.net/Articles/751951/rss

Alpine Linux-based postmarketOS is touch-optimized and pre-configured for
installation on smartphones and other mobile devices. The postmarketOS
blog introduces
which is a community project aimed at creating
free bootloaders and cellular modem firmware, currently focused on MediaTek
phones. “But before we get started, please keep in mind that these
are moon shots. So while there is some little progress, it’s mostly about
letting fellow hackers know what we’ve tried and what we’re up to, in the
hopes of attracting more interested talent to our cause. After all, our
philosophy is to keep the community informed and engaged during the
development phase!

COPPA Compliance

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/04/coppa_complianc.html

Interesting research: “‘Won’t Somebody Think of the Children?’ Examining COPPA Compliance at Scale“:

Abstract: We present a scalable dynamic analysis framework that allows for the automatic evaluation of the privacy behaviors of Android apps. We use our system to analyze mobile apps’ compliance with the Children’s Online Privacy Protection Act (COPPA), one of the few stringent privacy laws in the U.S. Based on our automated analysis of 5,855 of the most popular free children’s apps, we found that a majority are potentially in violation of COPPA, mainly due to their use of third-party SDKs. While many of these SDKs offer configuration options to respect COPPA by disabling tracking and behavioral advertising, our data suggest that a majority of apps either do not make use of these options or incorrectly propagate them across mediation SDKs. Worse, we observed that 19% of children’s apps collect identifiers or other personally identifiable information (PII) via SDKs whose terms of service outright prohibit their use in child-directed apps. Finally, we show that efforts by Google to limit tracking through the use of a resettable advertising ID have had little success: of the 3,454 apps that share the resettable ID with advertisers, 66% transmit other, non-resettable, persistent identifiers as well, negating any intended privacy-preserving properties of the advertising ID.

The answers to your questions for Eben Upton

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/eben-q-a-1/

Before Easter, we asked you to tell us your questions for a live Q & A with Raspberry Pi Trading CEO and Raspberry Pi creator Eben Upton. The variety of questions and comments you sent was wonderful, and while we couldn’t get to them all, we picked a handful of the most common to grill him on.

You can watch the video below — though due to this being the first pancake of our live Q&A videos, the sound is a bit iffy — or read Eben’s answers to the first five questions today. We’ll follow up with the rest in the next few weeks!

Live Q&A with Eben Upton, creator of the Raspberry Pi

Get your questions to us now using #AskRaspberryPi on Twitter

Any plans for 64-bit Raspbian?

Raspbian is effectively 32-bit Debian built for the ARMv6 instruction-set architecture supported by the ARM11 processor in the first-generation Raspberry Pi. So maybe the question should be: “Would we release a version of our operating environment that was built on top of 64-bit ARM Debian?”

And the answer is: “Not yet.”

When we released the Raspberry Pi 3 Model B+, we released an operating system image on the same day; the wonderful thing about that image is that it runs on every Raspberry Pi ever made. It even runs on the alpha boards from way back in 2011.

That deep backwards compatibility is really important for us, in large part because we don’t want to orphan our customers. If someone spent $35 on an older-model Raspberry Pi five or six years ago, they still spent $35, so it would be wrong for us to throw them under the bus.

So, if we were going to do a 64-bit version, we’d want to keep doing the 32-bit version, and then that would mean our efforts would be split across the two versions; and remember, we’re still a very small engineering team. Never say never, but it would be a big step for us.

For people wanting a 64-bit operating system, there are plenty of good third-party images out there, including SUSE Linux Enterprise Server.

Given that the 3B+ includes 5GHz wireless and Power over Ethernet (PoE) support, why would manufacturers continue to use the Compute Module?

It’s a form-factor thing.

Very large numbers of people are using the bigger product in an industrial context, and it’s well engineered for that: it has module certification, wireless on board, and now PoE support. But there are use cases that can’t accommodate this form factor. For example, NEC displays: we’ve had this great relationship with NEC for a couple of years now where a lot of their displays have a socket in the back that you can put a Compute Module into. That wouldn’t work with the 3B+ form factor.

Back of an NEC display with a Raspberry Pi Compute Module slotted in.

An NEC display with a Raspberry Pi Compute Module

What are some industrial uses/products Raspberry is used with?

The NEC displays are a good example of the broader trend of using Raspberry Pi in digital signage.

A Raspberry Pi running the wait time signage at The Wizarding World of Harry Potter, Universal Studios.
Image c/o thelonelyredditor1

If you see a monitor at a station, or an airport, or a recording studio, and you look behind it, it’s amazing how often you’ll find a Raspberry Pi sitting there. The original Raspberry Pi was particularly strong for multimedia use cases, so we saw uptake in signage very early on.

An array of many Raspberry Pis

Los Alamos Raspberry Pi supercomputer

Another great example is the Los Alamos National Laboratory building supercomputers out of Raspberry Pis. Many high-end supercomputers now are built using white-box hardware — just regular PCs connected together using some networking fabric — and a collection of Raspberry Pi units can serve as a scale model of that. The Raspberry Pi has less processing power, less memory, and less networking bandwidth than the PC, but it has a balanced amount of each. So if you don’t want to let your apprentice supercomputer engineers loose on your expensive supercomputer, a cluster of Raspberry Pis is a good alternative.

Why is there no power button on the Raspberry Pi?

“Once you start, where do you stop?” is a question we ask ourselves a lot.

There are a whole bunch of useful things that we haven’t included in the Raspberry Pi by default. We don’t have a power button, we don’t have a real-time clock, and we don’t have an analogue-to-digital converter — those are probably the three most common requests. And the issue with them is that they each cost a bit of money, they’re each only useful to a minority of users, and even that minority often can’t agree on exactly what they want. Some people would like a power button that is literally a physical analogue switch between the 5V input and the rest of the board, while others would like something a bit more like a PC power button, which is partway between a physical switch and a ‘shutdown’ button. There’s no consensus about what sort of power button we should add.

So the answer is: accessories. By leaving a feature off the board, we’re not taxing the majority of people who don’t want the feature. And of course, we create an opportunity for other companies in the ecosystem to create and sell accessories to those people who do want them.

Adafruit Push-button Power Switch Breakout Raspberry Pi

The Adafruit Push-button Power Switch Breakout is one of many accessories that fill in the gaps for makers.

We have this neat way of figuring out what features to include by default: we divide through the fraction of people who want it. If you have a 20 cent component that’s going to be used by a fifth of people, we treat that as if it’s a $1 component. And it has to fight its way against the $1 components that will be used by almost everybody.

Do you think that Raspberry Pi is the future of the Internet of Things?

Absolutely, Raspberry Pi is the future of the Internet of Things!

In practice, most of the viable early IoT use cases are in the commercial and industrial spaces rather than the consumer space. Maybe in ten years’ time, IoT will be about putting 10-cent chips into light switches, but right now there’s so much money to be saved by putting automation into factories that you don’t need 10-cent components to address the market. Last year, roughly 2 million $35 Raspberry Pi units went into commercial and industrial applications, and many of those are what you’d call IoT applications.

So I think we’re the future of a particular slice of IoT. And we have ten years to get our price point down to 10 cents 🙂

The post The answers to your questions for Eben Upton appeared first on Raspberry Pi.

Cloud Empire: Meet the Rebel Alliance

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/cloud-empire-meet-the-rebel-alliance/

Cloud Empire: Meet the Rebel Alliance

Last week Backblaze made the exciting announcement that through partnerships with Packet and ServerCentral, cloud computing is available to Backblaze B2 Cloud Storage customers.

Those of you familiar with cloud computing will understand the significance of this news. We are now offering the least expensive cloud storage + cloud computing available anywhere. You no longer have to submit to the lock-in tactics and exorbitant prices charged by the other big players in the cloud services biz.

As Robin Harris wrote in ZDNet about last week’s computing partners announcement, Cloud Empire: Meet the Rebel Alliance.

We understand that some of our cloud backup and storage customers might be unfamiliar with cloud computing. Backblaze made its name in cloud backup and object storage, and that’s what our customers know us for. In response to customers requests, we’ve directly connected our B2 cloud object storage with cloud compute providers. This adds the ability to use and run programs on data once it’s in the B2 cloud, opening up a world of new uses for B2. Just some of the possibilities include media transcoding and rendering, web hosting, application development and testing, business analytics, disaster recovery, on-demand computing capacity (cloud bursting), AI, and mobile and IoT applications.

The world has been moving to a multi-cloud / hybrid cloud world, and customers are looking for more choices than those offered by the existing cloud players. Our B2 compute partnerships build on our mission to offer cloud storage that’s astonishingly easy and low-cost. They enable our customers to move into a more flexible and affordable cloud services ecosystem that provides a greater variety of choices and costs far less. We believe we are helping to fulfill the promise of the internet by allowing customers to choose the best-of-breed services from the best vendors.

If You’re Not Familiar with Cloud Computing, Here’s a Quick Overview

Cloud computing is another component of cloud services, like object storage, that replicates in the cloud a basic function of a computer system. Think of services that operate in a cloud as an infinitely scalable version of what happens on your desktop computer. In your desktop computer you have computing/processing (CPU), fast storage (like an SSD), data storage (like your disk drive), and memory (RAM). Their counterparts in the cloud are computing (CPU), block storage (fast storage), object storage (data storage), and processing memory (RAM).

Computer building blocks

CPU, RAM, fast internal storage, and a hard drive are the basic building blocks of a computer
They also are the basic building blocks of cloud computing

Some customers require only some of these services, such as cloud storage. B2 as a standalone service has proven to be an outstanding solution for those customers interested in backing up or archiving data. There are many customers that would like additional capabilities, such as performing operations on that data once it’s in the cloud. They need object storage combined with computing.

With the just announced compute partnerships, Backblaze is able to offer computing services to anyone using B2. A direct connection between Backblaze’s and our partners’ data centers means that our customers can process data stored in B2 with high speed, low latency, and zero data transfer costs.

Backblaze, Packet and Server Central cloud compute workflow diagram

Cloud service providers package up CPU, storage, and memory into services that you can rent on an hourly basis
You can scale up and down and add or remove services as you need them

How Does Computing + B2 Work?

Those wanting to use B2 with computing will need to sign up for accounts with Backblaze and either Packet or ServerCentral. Packet customers need only select “SJC1” as their region and then get started. The process is also simple for ServerCentral customers — they just need to register with a ServerCentral account rep.

The direct connection between B2 and our compute partners means customers will experience very low latency (less than 10ms) between services. Even better, all data transfers between B2 and the compute partner are free. When combined with Backblaze B2, customers can obtain cloud computing services for as little as 50% of the cost of Amazon’s Elastic Compute Cloud (EC2).

Opening Up the Cloud “Walled Garden”

Traditionally, cloud vendors charge fees for customers to move data outside the “walled garden” of that particular vendor. These fees reach upwards of $0.12 per gigabyte (GB) for data egress. This large fee for customers accessing their own data restricts users from using a multi-cloud approach and taking advantage of less expensive or better performing options. With free transfers between B2 and Packet or ServerCentral, customers now have a predictable, scalable solution for computing and data storage while avoiding vendor lock in. Dropbox made waves when they saved $75 million by migrating off of AWS. Adding computing to B2 helps anyone interested in moving some or all of their computing off of AWS and thereby cutting their AWS bill by 50% or more.

What are the Advantages of Cloud Storage + Computing?

Using computing and storage in the cloud provide a number of advantages over using in-house resources.

  1. You don’t have to purchase the actual hardware, software licenses, and provide space and IT resources for the systems.
  2. Cloud computing is available with just a few minutes notice and you only pay for whatever period of time you need. You avoid having additional hardware on your balance sheet.
  3. Resources are in the cloud and can provide online services to customers, mobile users, and partners located anywhere in the world.
  4. You can isolate the work on these systems from your normal production environment, making them ideal for testing and trying out new applications and development projects.
  5. Computing resources scale when you need them to, providing temporary or ongoing extra resources for expected or unexpected demand.
  6. They can provide redundant and failover services when and if your primary systems are unavailable for whatever reason.

Where Can I Learn More?

We encourage B2 customers to explore the options available at our partner sites, Packet and ServerCentral. They are happy to help customers understand what services are available and how to get started.

We are excited to see what you build! And please tell us in the comments what you are doing or have planned with B2 + computing.

P.S. May the force be with all of us!

The post Cloud Empire: Meet the Rebel Alliance appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Интервю по Би Би Си нарушава медийните стандарти

Post Syndicated from nellyo original https://nellyo.wordpress.com/2018/04/10/bbc_sanction/

Британският регулатор за медии санкционира обществения доставчик Би Би Си.  Това е първият случай, в който се санкционира обществената медия, откакто регулирането беше поето от OFCOM. Случаят  е свързан със стандартите на поведение на водещия при интервю в ефир.

По сигнал на Media Guardian регулаторът санкционира Би Би Си за радиоинтервю с експерт в областта на климатичните промени. Според решението Би Би Си  не са се противопоставили в достатъчна степен на гледната точка на госта на предаването, който не признава климатичните промени, твърди, че според експертите на ООН числата не ги потвърждават и че през  последните  години не е имало увеличение на екстремните метеорологични явления. Това не е вярно,  както посочват от британската метеорологична служба.  Зелените също имат изказвания, че гостът има очевидно/демонстративно неверни твърдения. Но възгледите му не са оспорени от водещия.

Според регулатора това води до проблем с две изисквания: точност и безпристрастност : Би Би Си е трябвало  да гарантира, че темата е била отразена с необходимата точност и безпристрастност.


 Предаването не показва ясно на слушателите, че възгледите на [лорд Лоусън] за науката за изменението на климата противоречат на преобладаващото научно мнение в тази област.


  Според нас  водещият трябва да е подготвен да предложи аргументи против и контекст на вижданията на лорд Лоусън по подходящ начин.

Решението (стр.12-24)

Накратко, не може как да е в студио.

Ако си водещ, да говориш неверни неща е против правилата.

Сега се потвърждава, че да мълчиш, когато говорят неверни неща, също е против правилата.

American Public Television Embraces the Cloud — And the Future

Post Syndicated from Andy Klein original https://www.backblaze.com/blog/american-public-television-embraces-the-cloud-and-the-future/

American Public Television website

American Public Television was like many organizations that have been around for a while. They were entrenched using an older technology — in their case, tape storage and distribution — that once met their needs but was limiting their productivity and preventing them from effectively collaborating with their many media partners. APT’s VP of Technology knew that he needed to move into the future and embrace cloud storage to keep APT ahead of the game.
Since 1961, American Public Television (APT) has been a leading distributor of groundbreaking, high-quality, top-rated programming to the nation’s public television stations. Gerry Field is the Vice President of Technology at APT and is responsible for delivering their extensive program catalog to 350+ public television stations nationwide.

In the time since Gerry  joined APT in 2007, the industry has been in digital overdrive. During that time APT has continued to acquire and distribute the best in public television programming to their technically diverse subscribers.

This created two challenges for Gerry. First, new technology and format proliferation were driving dramatic increases in digital storage. Second, many of APT’s subscribers struggled to keep up with the rapidly changing industry. While some subscribers had state-of-the-art satellite systems to receive programming, others had to wait for the post office to drop off programs recorded on tape weeks earlier. With no slowdown on the horizon of innovation in the industry, Gerry knew that his storage and distribution systems would reach a crossroads in no time at all.

American Public Television logo

Living the tape paradigm

The digital media industry is only a few years removed from its film, and later videotape, roots. Tape was the input and the output of the industry for many years. As a consequence, the tools and workflows used by the industry were built and designed to work with tape. Over time, the “file” slowly replaced the tape as the object to be captured, edited, stored and distributed. Trouble was, many of the systems and more importantly workflows were based on processing tape, and these have proven to be hard to change.

At APT, Gerry realized the limits of the tape paradigm and began looking for technologies and solutions that enabled workflows based on file and object based storage and distribution.

Thinking file based storage and distribution

For data (digital media) storage, APT, like everyone else, started by installing onsite storage servers. As the amount of digital data grew, more storage was added. In addition, APT was expanding its distribution footprint by creating or partnering with distribution channels such as CreateTV and APT Worldwide. This dramatically increased the number of programming formats and the amount of data that had to be stored. As a consequence, updating, maintaining, and managing the APT storage systems was becoming a major challenge and a major resource hog.

APT Online

Knowing that his in-house storage system was only going to cost more time and money, Gerry decided it was time to look at cloud storage. But that wasn’t the only reason he looked at the cloud. While most people consider cloud storage as just a place to back up and archive files, Gerry was envisioning how the ubiquity of the cloud could help solve his distribution challenges. The trouble was the price of cloud storage from vendors like Amazon S3 and Microsoft Azure was a non-starter, especially for a non-profit. Then Gerry came across Backblaze. B2 Cloud Storage service met all of his performance requirements, and at $0.005/GB/month for storage and $0.01/GB for downloads it was nearly 75% less than S3 or Azure.

Gerry did the math and found that he could economically incorporate B2 Cloud Storage into his IT portfolio, using it for both program submission and for active storage and archiving of the APT programs. In addition, B2 now gives him the foundation necessary to receive and distribute programming content over the Internet. This is especially useful for organizations that can’t conveniently access satellite distribution systems. Not to mention downloading from the cloud is much faster than sending a tape through the mail.

Adding B2 Cloud Storage to their infrastructure has helped American Public Television address two key challenges. First, they now have “unlimited” storage in the cloud without having to add any hardware. In addition, with B2, they only pay for the storage they use. That means they don’t have to buy storage upfront trying to match the maximum amount of storage they’ll ever need. Second, by using B2 as a distribution source for their programming APT subscribers, especially the smaller and remote ones, can get content faster and more reliably without having to perform costly upgrades to their infrastructure.

The road ahead

As APT gets used to their file based infrastructure and workflow, there are a number of cost saving and income generating ideas they are pondering which are now worth considering. Here are a few:

Program Submissions — New content can be uploaded from anywhere using a web browser, an Internet connection, and a login. For example, a producer in Cambodia can upload their film to B2. From there the film is downloaded to an in-house system where it is processed and transcoded using compute. The finished film is added to the APT catalog and added to B2. Once there, the program is instantly available for subscribers to order and download.

“The affordability and performance of Backblaze B2 is what allowed us to make the B2 cloud part of the APT data storage and distribution strategy into the future.” — Gerry Field

Easier Previews — At any time, work in process or finished programs can be made available for download from the B2 cloud. One place this could be useful is where a subscriber needs to review a program to comply with local policies and practices before airing. In the old system, each “one-off” was a time consuming manual process.

Instant Subscriptions — There are many organizations such as schools and businesses that want to use just one episode of a desired show. With an e-commerce based website, current or even archived programming kept in B2 could be available to download or stream for a minimal charge.

At APT there were multiple technologies needed to make their file-based infrastructure work, but as Gerry notes, having an affordable, trustworthy, cloud storage service like B2 is one of the critical building blocks needed to make everything work together.

The post American Public Television Embraces the Cloud — And the Future appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

AWS Certificate Manager Launches Private Certificate Authority

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/aws-certificate-manager-launches-private-certificate-authority/

Today we’re launching a new feature for AWS Certificate Manager (ACM), Private Certificate Authority (CA). This new service allows ACM to act as a private subordinate CA. Previously, if a customer wanted to use private certificates, they needed specialized infrastructure and security expertise that could be expensive to maintain and operate. ACM Private CA builds on ACM’s existing certificate capabilities to help you easily and securely manage the lifecycle of your private certificates with pay as you go pricing. This enables developers to provision certificates in just a few simple API calls while administrators have a central CA management console and fine grained access control through granular IAM policies. ACM Private CA keys are stored securely in AWS managed hardware security modules (HSMs) that adhere to FIPS 140-2 Level 3 security standards. ACM Private CA automatically maintains certificate revocation lists (CRLs) in Amazon Simple Storage Service (S3) and lets administrators generate audit reports of certificate creation with the API or console. This service is packed full of features so let’s jump in and provision a CA.

Provisioning a Private Certificate Authority (CA)

First, I’ll navigate to the ACM console in my region and select the new Private CAs section in the sidebar. From there I’ll click Get Started to start the CA wizard. For now, I only have the option to provision a subordinate CA so we’ll select that and use my super secure desktop as the root CA and click Next. This isn’t what I would do in a production setting but it will work for testing out our private CA.

Now, I’ll configure the CA with some common details. The most important thing here is the Common Name which I’ll set as secure.internal to represent my internal domain.

Now I need to choose my key algorithm. You should choose the best algorithm for your needs but know that ACM has a limitation today that it can only manage certificates that chain up to to RSA CAs. For now, I’ll go with RSA 2048 bit and click Next.

In this next screen, I’m able to configure my certificate revocation list (CRL). CRLs are essential for notifying clients in the case that a certificate has been compromised before certificate expiration. ACM will maintain the revocation list for me and I have the option of routing my S3 bucket to a custome domain. In this case I’ll create a new S3 bucket to store my CRL in and click Next.

Finally, I’ll review all the details to make sure I didn’t make any typos and click Confirm and create.

A few seconds later and I’m greeted with a fancy screen saying I successfully provisioned a certificate authority. Hooray! I’m not done yet though. I still need to activate my CA by creating a certificate signing request (CSR) and signing that with my root CA. I’ll click Get started to begin that process.

Now I’ll copy the CSR or download it to a server or desktop that has access to my root CA (or potentially another subordinate – so long as it chains to a trusted root for my clients).

Now I can use a tool like openssl to sign my cert and generate the certificate chain.

$openssl ca -config openssl_root.cnf -extensions v3_intermediate_ca -days 3650 -notext -md sha256 -in csr/CSR.pem -out certs/subordinate_cert.pem
Using configuration from openssl_root.cnf
Enter pass phrase for /Users/randhunt/dev/amzn/ca/private/root_private_key.pem:
Check that the request matches the signature
Signature ok
The Subject's Distinguished Name is as follows
stateOrProvinceName   :ASN.1 12:'Washington'
localityName          :ASN.1 12:'Seattle'
organizationName      :ASN.1 12:'Amazon'
organizationalUnitName:ASN.1 12:'Engineering'
commonName            :ASN.1 12:'secure.internal'
Certificate is to be certified until Mar 31 06:05:30 2028 GMT (3650 days)
Sign the certificate? [y/n]:y

1 out of 1 certificate requests certified, commit? [y/n]y
Write out database with 1 new entries
Data Base Updated

After that I’ll copy my subordinate_cert.pem and certificate chain back into the console. and click Next.

Finally, I’ll review all the information and click Confirm and import. I should see a screen like the one below that shows my CA has been activated successfully.

Now that I have a private CA we can provision private certificates by hopping back to the ACM console and creating a new certificate. After clicking create a new certificate I’ll select the radio button Request a private certificate then I’ll click Request a certificate.

From there it’s just similar to provisioning a normal certificate in ACM.

Now I have a private certificate that I can bind to my ELBs, CloudFront Distributions, API Gateways, and more. I can also export the certificate for use on embedded devices or outside of ACM managed environments.

Available Now
ACM Private CA is a service in and of itself and it is packed full of features that won’t fit into a blog post. I strongly encourage the interested readers to go through the developer guide and familiarize themselves with certificate based security. ACM Private CA is available in in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), EU (Frankfurt) and EU (Ireland). Private CAs cost $400 per month (prorated) for each private CA. You are not charged for certificates created and maintained in ACM but you are charged for certificates where you have access to the private key (exported or created outside of ACM). The pricing per certificate is tiered starting at $0.75 per certificate for the first 1000 certificates and going down to $0.001 per certificate after 10,000 certificates.

I’m excited to see administrators and developers take advantage of this new service. As always please let us know what you think of this service on Twitter or in the comments below.


AWS Secrets Manager: Store, Distribute, and Rotate Credentials Securely

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/aws-secrets-manager-store-distribute-and-rotate-credentials-securely/

Today we’re launching AWS Secrets Manager which makes it easy to store and retrieve your secrets via API or the AWS Command Line Interface (CLI) and rotate your credentials with built-in or custom AWS Lambda functions. Managing application secrets like database credentials, passwords, or API Keys is easy when you’re working locally with one machine and one application. As you grow and scale to many distributed microservices, it becomes a daunting task to securely store, distribute, rotate, and consume secrets. Previously, customers needed to provision and maintain additional infrastructure solely for secrets management which could incur costs and introduce unneeded complexity into systems.

AWS Secrets Manager

Imagine that I have an application that takes incoming tweets from Twitter and stores them in an Amazon Aurora database. Previously, I would have had to request a username and password from my database administrator and embed those credentials in environment variables or, in my race to production, even in the application itself. I would also need to have our social media manager create the Twitter API credentials and figure out how to store those. This is a fairly manual process, involving multiple people, that I have to restart every time I want to rotate these credentials. With Secrets Manager my database administrator can provide the credentials in secrets manager once and subsequently rely on a Secrets Manager provided Lambda function to automatically update and rotate those credentials. My social media manager can put the Twitter API keys in Secrets Manager which I can then access with a simple API call and I can even rotate these programmatically with a custom lambda function calling out to the Twitter API. My secrets are encrypted with the KMS key of my choice, and each of these administrators can explicitly grant access to these secrets with with granular IAM policies for individual roles or users.

Let’s take a look at how I would store a secret using the AWS Secrets Manager console. First, I’ll click Store a new secret to get to the new secrets wizard. For my RDS Aurora instance it’s straightforward to simply select the instance and provide the initial username and password to connect to the database.

Next, I’ll fill in a quick description and a name to access my secret by. You can use whatever naming scheme you want here.

Next, we’ll configure rotation to use the Secrets Manager-provided Lambda function to rotate our password every 10 days.

Finally, we’ll review all the details and check out our sample code for storing and retrieving our secret!

Finally I can review the secrets in the console.

Now, if I needed to access these secrets I’d simply call the API.

import json
import boto3
secrets = boto3.client("secretsmanager")
rds = json.dumps(secrets.get_secrets_value("prod/TwitterApp/Database")['SecretString'])

Which would give me the following values:

{'engine': 'mysql',
 'host': 'twitterapp2.abcdefg.us-east-1.rds.amazonaws.com',
 'password': '-)Kw>THISISAFAKEPASSWORD:lg{&sad+Canr',
 'port': 3306,
 'username': 'ranman'}

More than passwords

AWS Secrets Manager works for more than just passwords. I can store OAuth credentials, binary data, and more. Let’s look at storing my Twitter OAuth application keys.

Now, I can define the rotation for these third-party OAuth credentials with a custom AWS Lambda function that can call out to Twitter whenever we need to rotate our credentials.

Custom Rotation

One of the niftiest features of AWS Secrets Manager is custom AWS Lambda functions for credential rotation. This allows you to define completely custom workflows for credentials. Secrets Manager will call your lambda with a payload that includes a Step which specifies which step of the rotation you’re in, a SecretId which specifies which secret the rotation is for, and importantly a ClientRequestToken which is used to ensure idempotency in any changes to the underlying secret.

When you’re rotating secrets you go through a few different steps:

  1. createSecret
  2. setSecret
  3. testSecret
  4. finishSecret

The advantage of these steps is that you can add any kind of approval steps you want for each phase of the rotation. For more details on custom rotation check out the documentation.

Available Now
AWS Secrets Manager is available today in US East (N. Virginia), US East (Ohio), US West (N. California), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), EU (Frankfurt), EU (Ireland), EU (London), and South America (São Paulo). Secrets are priced at $0.40 per month per secret and $0.05 per 10,000 API calls. I’m looking forward to seeing more users adopt rotating credentials to secure their applications!


Ефектите на медийната консолидация: по сценарий

Post Syndicated from nellyo original https://nellyo.wordpress.com/2018/04/04/sinclair-2/

 Sinclair Broadcast Group е най-голяма тв група по брой медии и също така по покритие в САЩ. Известни са с  новинарско съдържание и предавания, които популяризират консервативни политически позиции  и са в подкрепа на Републиканската партия.

 Когато Тръмп каже следното –


– ето какво следва в медиите на Синклер  – каскада от еднотипни изпълнения по сценарий, както се вижда във видеото:

“Някои  медии  използват своите платформи, за да наложат своето лично пристрастие. Това е изключително опасно за нашата демокрация.”

Видеото е публикувано и в NYT:


Показва ефектите  на медийната консолидация за правото на информация.  А също показва и как това управление означава критиката като fake.

+ op-ed от вчера


Amazon Transcribe Now Generally Available

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/amazon-transcribe-now-generally-available/

At AWS re:Invent 2017 we launched Amazon Transcribe in private preview. Today we’re excited to make Amazon Transcribe generally available for all developers. Amazon Transcribe is an automatic speech recognition service (ASR) that makes it easy for developers to add speech to text capabilities to their applications. We’ve iterated on customer feedback in the preview to make a number of enhancements to Amazon Transcribe.

New Amazon Transcribe Features in GA

To start off we’ve made the SampleRate parameter optional which means you only need to know the file type of your media and the input language. We’ve added two new features – the ability to differentiate multiple speakers in the audio to provide more intelligible transcripts (“who spoke when”), and a custom vocabulary to improve the accuracy of speech recognition for product names, industry-specific terminology, or names of individuals. To refresh our memories on how Amazon Transcribe works lets look at a quick example. I’ll convert this audio in my S3 bucket.

import boto3
transcribe = boto3.client("transcribe")
    Media={"MediaFileUri": "https://s3.amazonaws.com/randhunt-transcribe-demo-us-east-1/out.mp3"}

This will output JSON similar to this (I’ve stripped out most of the response) with indidivudal speakers identified:

  "jobName": "reinvent",
  "accountId": "1234",
  "results": {
    "transcripts": [
        "transcript": "Hi, everybody, i'm randall ..."
    "speaker_labels": {
      "speakers": 2,
      "segments": [
          "start_time": "0.000000",
          "speaker_label": "spk_0",
          "end_time": "0.010",
          "items": []
          "start_time": "0.010000",
          "speaker_label": "spk_1",
          "end_time": "4.990",
          "items": [
              "start_time": "1.000",
              "speaker_label": "spk_1",
              "end_time": "1.190"
              "start_time": "1.190",
              "speaker_label": "spk_1",
              "end_time": "1.700"
    "items": [
        "start_time": "1.000",
        "end_time": "1.190",
        "alternatives": [
            "confidence": "0.9971",
            "content": "Hi"
        "type": "pronunciation"
        "alternatives": [
            "content": ","
        "type": "punctuation"
        "start_time": "1.190",
        "end_time": "1.700",
        "alternatives": [
            "confidence": "1.0000",
            "content": "everybody"
        "type": "pronunciation"
  "status": "COMPLETED"

Custom Vocabulary

Now if I needed to have a more complex technical discussion with a colleague I could create a custom vocabulary. A custom vocabulary is specified as an array of strings passed to the CreateVocabulary API and you can include your custom vocabulary in a transcription job by passing in the name as part of the Settings in a StartTranscriptionJob API call. An individual vocabulary can be as large as 50KB and each phrase must be less than 256 characters. If I wanted to transcribe the recordings of my highschool AP Biology class I could create a custom vocabulary in Python like this:

import boto3
transcribe = boto3.client("transcribe")

I can refer to this vocabulary later on by the name APBiology and update it programatically based on any errors I may find in the transcriptions.

Available Now

Amazon Transcribe is available now in US East (N. Virginia), US West (Oregon), US East (Ohio) and EU (Ireland). Transcribe’s free tier gives you 60 minutes of transcription for free per month for the first 12 months with a pay-as-you-go model of $0.0004 per second of transcribed audio after that, with a minimum charge of 15 seconds.

When combined with other tools and services I think transcribe opens up a entirely new opportunities for application development. I’m excited to see what technologies developers build with this new service.


Backblaze Announces B2 Compute Partnerships

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/introducing-cloud-compute-services/

Backblaze Announces B2 Compute Partnerships

In 2015, we announced Backblaze B2 Cloud Storage — the most affordable, high performance storage cloud on the planet. The decision to release B2 as a service was in direct response to customers asking us if they could use the same cloud storage infrastructure we use for our Computer Backup service. With B2, we entered a market in direct competition with Amazon S3, Google Cloud Services, and Microsoft Azure Storage. Today, we have over 500 petabytes of data from customers in over 150 countries. At $0.005 / GB / month for storage (1/4th of S3) and $0.01 / GB for downloads (1/5th of S3), it turns out there’s a healthy market for cloud storage that’s easy and affordable.

As B2 has grown, customers wanted to use our cloud storage for a variety of use cases that required not only storage but compute. We’re happy to say that through partnerships with Packet & ServerCentral, today we’re announcing that compute is now available for B2 customers.

Cloud Compute and Storage

Backblaze has directly connected B2 with the compute servers of Packet and ServerCentral, thereby allowing near-instant (< 10 ms) data transfers between services. Also, transferring data between B2 and both our compute partners is free.

  • Storing data in B2 and want to run an AI analysis on it? — There are no fees to move the data to our compute partners.
  • Generating data in an application? — Run the application with one of our partners and store it in B2.
  • Transfers are free and you’ll save more than 50% off of the equivalent set of services from AWS.

These partnerships enable B2 customers to use compute, give our compute partners’ customers access to cloud storage, and introduce new customers to industry-leading storage and compute — all with high-performance, low-latency, and low-cost.

Is This a Big Deal? We Think So

Compute is one of the most requested services from our customers Why? Because it unlocks a number of use cases for them. Let’s look at three popular examples:

Transcoding Media Files

B2 has earned wide adoption in the Media & Entertainment (“M&E”) industry. Our affordable storage and download pricing make B2 great for a wide variety of M&E use cases. But many M&E workflows require compute. Content syndicators, like American Public Television, need the ability to transcode files to meet localization and distribution management requirements.

There are a multitude of reasons that transcode is needed — thumbnail and proxy generation enable M&E professionals to work efficiently. Without compute, the act of transcoding files remains cumbersome. Either the files need to be brought down from the cloud, transcoded, and then pushed back up or they must be kept locally until the project is complete. Both scenarios are inefficient.

Starting today, any content producer can spin up compute with one of our partners, pay by the hour for their transcode processing, and return the new media files to B2 for storage and distribution. The company saves money, moves faster, and ensures their files are safe and secure.

Disaster Recovery

Backblaze’s heritage is based on providing outstanding backup services. When you have incredibly affordable cloud storage, it ends up being a great destination for your backup data.

Most enterprises have virtual machines (“VMs”) running in their infrastructure and those VMs need to be backed up. In a disaster scenario, a business wants to know they can get back up and running quickly.

With all data stored in B2, a business can get up and running quickly. Simply restore your backed up VM to one of our compute providers, and your business will be able to get back online.

Since B2 does not place restrictions, delays, or penalties on getting data out, customers can get back up and running quickly and affordably.

Saving $74 Million (aka “The Dropbox Effect”)

Ten years ago, Backblaze decided that S3 was too costly a platform to build its cloud storage business. Instead, we created the Backblaze Storage Pod and our own cloud storage infrastructure. That decision enabled us to offer our customers storage at a previously unavailable price point and maintain those prices for over a decade. It also laid the foundation for Netflix Open Connect and Facebook Open Compute.

Dropbox recently migrated the majority of their cloud services off of AWS and onto Dropbox’s own infrastructure. By leaving AWS, Dropbox was able to build out their own data centers and still save over $74 Million. They achieved those savings by avoiding the fees AWS charges for storing and downloading data, which, incidentally, are five times higher than Backblaze B2.

For Dropbox, being able to realize savings was possible because they have access to enough capital and expertise that they can build out their own infrastructure. For companies that have such resources and scale, that’s a great answer.

“Before this offering, the economics of the cloud would have made our business simply unviable.” — Gabriel Menegatti, SlicingDice

The questions Backblaze and our compute partners pondered was “how can we democratize the Dropbox effect for our storage and compute customers? How can we help customers do more and pay less?” The answer we came up with was to connect Backblaze’s B2 storage with strategic compute partners and remove any transfer fees between them. You may not save $74 million as Dropbox did, but you can choose the optimal providers for your use case and realize significant savings in the process.

This Sounds Good — Tell Me More About Your Partners

We’re very fortunate to be launching our compute program with two fantastic partners in Packet and ServerCentral. These partners allow us to offer a range of computing services.


We recommend Packet for customers that need on-demand, high performance, bare metal servers available by the hour. They also have robust offerings for private / customized deployments. Their offerings end up costing 50-75% of the equivalent offerings from EC2.

To get started with Packet and B2, visit our partner page on Packet.net.


ServerCentral is the right partner for customers that have business and IT challenges that require more than “just” hardware. They specialize in fully managed, custom cloud solutions that solve complex business and IT challenges. ServerCentral also has expertise in managed network solutions to address global connectivity and content delivery.

To get started with ServerCentral and B2, visit our partner page on ServerCentral.com.

What’s Next?

We’re excited to find out. The combination of B2 and compute unlocks use cases that were previously impossible or at least unaffordable.

“The combination of performance and price offered by this partnership enables me to create an entirely new business line. Before this offering, the economics of the cloud would have made our business simply unviable,” noted Gabriel Menegatti, co-founder at SlicingDice, a serverless data warehousing service. “Knowing that transfers between compute and B2 are free means I don’t have to worry about my business being successful. And, with download pricing from B2 at just $0.01 GB, I know I’m avoiding a 400% tax from AWS on data I retrieve.”

What can you do with B2 & compute? Please share your ideas with us in the comments. And, for those attending NAB 2018 in Las Vegas next week, please come by and say hello!

The post Backblaze Announces B2 Compute Partnerships appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

A geometric Rust adventure

Post Syndicated from Eevee original https://eev.ee/blog/2018/03/30/a-geometric-rust-adventure/

Hi. Yes. Sorry. I’ve been trying to write this post for ages, but I’ve also been working on a huge writing project, and apparently I have a very limited amount of writing mana at my disposal. I think this is supposed to be a Patreon reward from January. My bad. I hope it’s super great to make up for the wait!

I recently ported some math code from C++ to Rust in an attempt to do a cool thing with Doom. Here is my story.

The problem

I presented it recently as a conundrum (spoilers: I solved it!), but most of those details are unimportant.

The short version is: I have some shapes. I want to find their intersection.

Really, I want more than that: I want to drop them all on a canvas, intersect everything with everything, and pluck out all the resulting polygons. The input is a set of cookie cutters, and I want to press them all down on the same sheet of dough and figure out what all the resulting contiguous pieces are. And I want to know which cookie cutter(s) each piece came from.

But intersection is a good start.

Example of the goal.  Given two squares that overlap at their corners, I want to find the small overlap piece, plus the two L-shaped pieces left over from each square

I’m carefully referring to the input as shapes rather than polygons, because each one could be a completely arbitrary collection of lines. Obviously there’s not much you can do with shapes that aren’t even closed, but at the very least, I need to handle concavity and multiple disconnected polygons that together are considered a single input.

This is a non-trivial problem with a lot of edge cases, and offhand I don’t know how to solve it robustly. I’m not too eager to go figure it out from scratch, so I went hunting for something I could build from.

(Infuriatingly enough, I can just dump all the shapes out in an SVG file and any SVG viewer can immediately solve the problem, but that doesn’t quite help me. Though I have had a few people suggest I just rasterize the whole damn problem, and after all this, I’m starting to think they may have a point.)

Alas, I couldn’t find a Rust library for doing this. I had a hard time finding any library for doing this that wasn’t a massive fully-featured geometry engine. (I could’ve used that, but I wanted to avoid non-Rust dependencies if possible, since distributing software is already enough of a nightmare.)

A Twitter follower directed me towards a paper that described how to do very nearly what I wanted and nothing else: “A simple algorithm for Boolean operations on polygons” by F. Martínez (2013). Being an academic paper, it’s trapped in paywall hell; sorry about that. (And as I understand it, none of the money you’d pay to get the paper would even go to the authors? Is that right? What a horrible and predatory system for discovering and disseminating knowledge.)

The paper isn’t especially long, but it does describe an awful lot of subtle details and is mostly written in terms of its own reference implementation. Rather than write my own implementation based solely on the paper, I decided to try porting the reference implementation from C++ to Rust.

And so I fell down the rabbit hole.

The basic algorithm

Thankfully, the author has published the sample code on his own website, if you want to follow along. (It’s the bottom link; the same author has, confusingly, published two papers on the same topic with similar titles, four years apart.)

If not, let me describe the algorithm and how the code is generally laid out. The algorithm itself is based on a sweep line, where a vertical line passes across the plane and ✨ does stuff ✨ as it encounters various objects. This implementation has no physical line; instead, it keeps track of which segments from the original polygon would be intersecting the sweep line, which is all we really care about.

A vertical line is passing rightwards over a couple intersecting shapes.  The line current intersects two of the shapes' sides, and these two sides are the "sweep list"

The code is all bundled inside a class with only a single public method, run, because… that’s… more object-oriented, I guess. There are several helper methods, and state is stored in some attributes. A rough outline of run is:

  1. Run through all the line segments in both input polygons. For each one, generate two SweepEvents (one for each endpoint) and add them to a std::deque for storage.

    Add pointers to the two SweepEvents to a std::priority_queue, the event queue. This queue uses a custom comparator to order the events from left to right, so the top element is always the leftmost endpoint.

  2. Loop over the event queue (where an “event” means the sweep line passed over the left or right end of a segment). Encountering a left endpoint means the sweep line is newly touching that segment, so add it to a std::set called the sweep list. An important point is that std::set is ordered, and the sweep list uses a comparator that keeps segments in order vertically.

    Encountering a right endpoint means the sweep line is leaving a segment, so that segment is removed from the sweep list.

  3. When a segment is added to the sweep list, it may have up to two neighbors: the segment above it and the segment below it. Call possibleIntersection to check whether it intersects either of those neighbors. (This is nearly sufficient to find all intersections, which is neat.)

  4. If possibleIntersection detects an intersection, it will split each segment into two pieces then and there. The old segment is shortened in-place to become the left part, and a new segment is created for the right part. The new endpoints at the point of intersection are added to the event queue.

  5. Some bookkeeping is done along the way to track which original polygons each segment is inside, and eventually the segments are reconstructed into new polygons.

Hopefully that’s enough to follow along. It took me an inordinately long time to tease this out. The comments aren’t especially helpful.

    std::deque<SweepEvent> eventHolder;    // It holds the events generated during the computation of the boolean operation

Syntax and basic semantics

The first step was to get something that rustc could at least parse, which meant translating C++ syntax to Rust syntax.

This was surprisingly straightforward! C++ classes become Rust structs. (There was no inheritance here, thankfully.) All the method declarations go away. Method implementations only need to be indented and wrapped in impl.

I did encounter some unnecessarily obtuse uses of the ternary operator:

(prevprev != sl.begin()) ? --prevprev : prevprev = sl.end();

Rust doesn’t have a ternary — you can use a regular if block as an expression — so I expanded these out.

C++ switch blocks become Rust match blocks, but otherwise function basically the same. Rust’s enums are scoped (hallelujah), so I had to explicitly spell out where enum values came from.

The only really annoying part was changing function signatures; C++ types don’t look much at all like Rust types, save for the use of angle brackets. Rust also doesn’t pass by implicit reference, so I needed to sprinkle a few &s around.

I would’ve had a much harder time here if this code had relied on any remotely esoteric C++ functionality, but thankfully it stuck to pretty vanilla features.

Language conventions

This is a geometry problem, so the sample code unsurprisingly has its own home-grown point type. Rather than port that type to Rust, I opted to use the popular euclid crate. Not only is it code I didn’t have to write, but it already does several things that the C++ code was doing by hand inline, like dot products and cross products. And all I had to do was add one line to Cargo.toml to use it! I have no idea how anyone writes C or C++ without a package manager.

The C++ code used getters, i.e. point.x (). I’m not a huge fan of getters, though I do still appreciate the need for them in lowish-level systems languages where you want to future-proof your API and the language wants to keep a clear distinction between attribute access and method calls. But this is a point, which is nothing more than two of the same numeric type glued together; what possible future logic might you add to an accessor? The euclid authors appear to side with me and leave the coordinates as public fields, so I took great joy in removing all the superfluous parentheses.

Polygons are represented with a Polygon class, which has some number of Contours. A contour is a single contiguous loop. Something you’d usually think of as a polygon would only have one, but a shape with a hole would have two: one for the outside, one for the inside. The weird part of this arrangement was that Polygon implemented nearly the entire STL container interface, then waffled between using it and not using it throughout the rest of the code. Rust lets anything in the same module access non-public fields, so I just skipped all that and used polygon.contours directly. Hell, I think I made contours public.

Finally, the SweepEvent type has a pol field that’s declared as an enum PolygonType (either SUBJECT or CLIPPING, to indicate which of the two inputs it is), but then some other code uses the same field as a numeric index into a polygon’s contours. Boy I sure do love static typing where everything’s a goddamn integer. I wanted to extend the algorithm to work on arbitrarily many input polygons anyway, so I scrapped the enum and this became a usize.

Then I got to all the uses of STL. I have only a passing familiarity with the C++ standard library, and this code actually made modest use of it, which caused some fun days-long misunderstandings.

As mentioned, the SweepEvents are stored in a std::deque, which is never read from. It took me a little thinking to realize that the deque was being used as an arena: it’s the canonical home for the structs so pointers to them can be tossed around freely. (It can’t be a std::vector, because that could reallocate and invalidate all the pointers; std::deque is probably a doubly-linked list, and guarantees no reallocation.)

Rust’s standard library does have a doubly-linked list type, but I knew I’d run into ownership hell here later anyway, so I think I replaced it with a Rust Vec to start with. It won’t compile either way, so whatever. We’ll get back to this in a moment.

The list of segments currently intersecting the sweep line is stored in a std::set. That type is explicitly ordered, which I’m very glad I knew already. Rust has two set types, HashSet and BTreeSet; unsurprisingly, the former is unordered and the latter is ordered. Dropping in BTreeSet and fixing some method names got me 90% of the way there.

Which brought me to the other 90%. See, the C++ code also relies on finding nodes adjacent to the node that was just inserted, via STL iterators.

next = prev = se->posSL = it = sl.insert(se).first;
(prev != sl.begin()) ? --prev : prev = sl.end();

I freely admit I’m bad at C++, but this seems like something that could’ve used… I don’t know, 1 comment. Or variable names more than two letters long. What it actually does is:

  1. Add the current sweep event (se) to the sweep list (sl), which returns a pair whose first element is an iterator pointing at the just-inserted event.

  2. Copies that iterator to several other variables, including prev and next.

  3. If the event was inserted at the beginning of the sweep list, set prev to the sweep list’s end iterator, which in C++ is a legal-but-invalid iterator meaning “the space after the end” or something. This is checked for in later code, to see if there is a previous event to look at. Otherwise, decrement prev, so it’s now pointing at the event immediately before the inserted one.

  4. Increment next normally. If the inserted event is last, then this will bump next to the end iterator anyway.

In other words, I need to get the previous and next elements from a BTreeSet. Rust does have bidirectional iterators, which BTreeSet supports… but BTreeSet::insert only returns a bool telling me whether or not anything was inserted, not the position. I came up with this:

let mut maybe_below = active_segments.range(..segment).last().map(|v| *v);
let mut maybe_above = active_segments.range(segment..).next().map(|v| *v);

The range method returns an iterator over a subset of the tree. The .. syntax makes a range (where the right endpoint is exclusive), so ..segment finds the part of the tree before the new segment, and segment.. finds the part of the tree after it. (The latter would start with the segment itself, except I haven’t inserted it yet, so it’s not actually there.)

Then the standard next() and last() methods on bidirectional iterators find me the element I actually want. But the iterator might be empty, so they both return an Option. Also, iterators tend to return references to their contents, but in this case the contents are already references, and I don’t want a double reference, so the map call dereferences one layer — but only if the Option contains a value. Phew!

This is slightly less efficient than the C++ code, since it has to look up where segment goes three times rather than just one. I might be able to get it down to two with some more clever finagling of the iterator, but microsopic performance considerations were a low priority here.

Finally, the event queue uses a std::priority_queue to keep events in a desired order and efficiently pop the next one off the top.

Except priority queues act like heaps, where the greatest (i.e., last) item is made accessible.

Sorting out sorting

C++ comparison functions return true to indicate that the first argument is less than the second argument. Sweep events occur from left to right. You generally implement sorts so that the first thing comes, erm, first.

But sweep events go in a priority queue, and priority queues surface the last item, not the first. This C++ code handled this minor wrinkle by implementing its comparison backwards.

struct SweepEventComp : public std::binary_function<SweepEvent, SweepEvent, bool> { // for sorting sweep events
// Compare two sweep events
// Return true means that e1 is placed at the event queue after e2, i.e,, e1 is processed by the algorithm after e2
bool operator() (const SweepEvent* e1, const SweepEvent* e2)
    if (e1->point.x () > e2->point.x ()) // Different x-coordinate
        return true;
    if (e2->point.x () > e1->point.x ()) // Different x-coordinate
        return false;
    if (e1->point.y () != e2->point.y ()) // Different points, but same x-coordinate. The event with lower y-coordinate is processed first
        return e1->point.y () > e2->point.y ();
    if (e1->left != e2->left) // Same point, but one is a left endpoint and the other a right endpoint. The right endpoint is processed first
        return e1->left;
    // Same point, both events are left endpoints or both are right endpoints.
    if (signedArea (e1->point, e1->otherEvent->point, e2->otherEvent->point) != 0) // not collinear
        return e1->above (e2->otherEvent->point); // the event associate to the bottom segment is processed first
    return e1->pol > e2->pol;

Maybe it’s just me, but I had a hell of a time just figuring out what problem this was even trying to solve. I still have to reread it several times whenever I look at it, to make sure I’m getting the right things backwards.

Making this even more ridiculous is that there’s a second implementation of this same sort, with the same name, in another file — and that one’s implemented forwards. And doesn’t use a tiebreaker. I don’t entirely understand how this even compiles, but it does!

I painstakingly translated this forwards to Rust. Unlike the STL, Rust doesn’t take custom comparators for its containers, so I had to implement ordering on the types themselves (which makes sense, anyway). I wrapped everything in the priority queue in a Reverse, which does what it sounds like.

I’m fairly pleased with Rust’s ordering model. Most of the work is done in Ord, a trait with a cmp() method returning an Ordering (one of Less, Equal, and Greater). No magic numbers, no need to implement all six ordering methods! It’s incredible. Ordering even has some handy methods on it, so the usual case of “order by this, then by this” can be written as:

return self.point().x.cmp(&other.point().x)

Well. Just kidding! It’s not quite that easy. You see, the points here are composed of floats, and floats have the fun property that not all of them are comparable. Specifically, NaN is not less than, greater than, or equal to anything else, including itself. So IEEE 754 float ordering cannot be expressed with Ord. Unless you want to just make up an answer for NaN, but Rust doesn’t tend to do that.

Rust’s float types thus implement the weaker PartialOrd, whose method returns an Option<Ordering> instead. That makes the above example slightly uglier:

return self.point().x.partial_cmp(&other.point().x).unwrap()

Also, since I use unwrap() here, this code will panic and take the whole program down if the points are infinite or NaN. Don’t do that.

This caused some minor inconveniences in other places; for example, the general-purpose cmp::min() doesn’t work on floats, because it requires an Ord-erable type. Thankfully there’s a f64::min(), which handles a NaN by returning the other argument.

(Cool story: for the longest time I had this code using f32s. I’m used to translating int to “32 bits”, and apparently that instinct kicked in for floats as well, even floats spelled double.)

The only other sorting adventure was this:

// Due to overlapping edges the resultEvents array can be not wholly sorted
bool sorted = false;
while (!sorted) {
    sorted = true;
    for (unsigned int i = 0; i < resultEvents.size (); ++i) {
        if (i + 1 < resultEvents.size () && sec (resultEvents[i], resultEvents[i+1])) {
            std::swap (resultEvents[i], resultEvents[i+1]);
            sorted = false;

(I originally misread this comment as saying “the array cannot be wholly sorted” and had no idea why that would be the case, or why the author would then immediately attempt to bubble sort it.)

I’m still not sure why this uses an ad-hoc sort instead of std::sort. But I’m used to taking for granted that general-purpose sorting implementations are tuned to work well for almost-sorted data, like Python’s. Maybe C++ is untrustworthy here, for some reason. I replaced it with a call to .sort() and all seemed fine.

Phew! We’re getting there. Finally, my code appears to type-check.

But now I see storm clouds gathering on the horizon.

Ownership hell

I have a problem. I somehow run into this problem every single time I use Rust. The solutions are never especially satisfying, and all the hacks I might use if forced to write C++ turn out to be unsound, which is even more annoying because rustc is just sitting there with this smug “I told you so expression” and—

The problem is ownership, which Rust is fundamentally built on. Any given value must have exactly one owner, and Rust must be able to statically convince itself that:

  1. No reference to a value outlives that value.
  2. If a mutable reference to a value exists, no other references to that value exist at the same time.

This is the core of Rust. It guarantees at compile time that you cannot lose pointers to allocated memory, you cannot double-free, you cannot have dangling pointers.

It also completely thwarts a lot of approaches you might be inclined to take if you come from managed languages (where who cares, the GC will take care of it) or C++ (where you just throw pointers everywhere and hope for the best apparently).

For example, pointer loops are impossible. Rust’s understanding of ownership and lifetimes is hierarchical, and it simply cannot express loops. (Rust’s own doubly-linked list type uses raw pointers and unsafe code under the hood, where “unsafe” is an escape hatch for the usual ownership rules. Since I only recently realized that pointers to the inside of a mutable Vec are a bad idea, I figure I should probably not be writing unsafe code myself.)

This throws a few wrenches in the works.

Problem the first: pointer loops

I immediately ran into trouble with the SweepEvent struct itself. A SweepEvent pulls double duty: it represents one endpoint of a segment, but each left endpoint also handles bookkeeping for the segment itself — which means that most of the fields on a right endpoint are unused. Also, and more importantly, each SweepEvent has a pointer to the corresponding SweepEvent at the other end of the same segment. So a pair of SweepEvents point to each other.

Rust frowns upon this. In retrospect, I think I could’ve kept it working, but I also think I’m wrong about that.

My first step was to wrench SweepEvent apart. I moved all of the segment-stuff (which is virtually all of it) into a single SweepSegment type, and then populated the event queue with a SweepEndpoint tuple struct, similar to:

enum SegmentEnd {

struct SweepEndpoint<'a>(&'a SweepSegment, SegmentEnd);

This makes SweepEndpoint essentially a tuple with a name. The 'a is a lifetime and says, more or less, that a SweepEndpoint cannot outlive the SweepSegment it references. Makes sense.

Problem solved! I no longer have mutually referential pointers. But I do still have pointers (well, references), and they have to point to something.

Problem the second: where’s all the data

Which brings me to the problem I always run into with Rust. I have a bucket of things, and I need to refer to some of them multiple times.

I tried half a dozen different approaches here and don’t clearly remember all of them, but I think my core problem went as follows. I translated the C++ class to a Rust struct with some methods hanging off of it. A simplified version might look like this.

struct Algorithm {
    arena: LinkedList<SweepSegment>,
    event_queue: BinaryHeap<SweepEndpoint>,

Ah, hang on — SweepEndpoint needs to be annotated with a lifetime, so Rust can enforce that those endpoints don’t live longer than the segments they refer to. No problem?

struct Algorithm<'a> {
    arena: LinkedList<SweepSegment>,
    event_queue: BinaryHeap<SweepEndpoint<'a>>,

Okay! Now for some methods.

fn run(&mut self) {
    self.arena.push_back(SweepSegment{ data: 5 });
    self.event_queue.push(SweepEndpoint(self.arena.back().unwrap(), SegmentEnd::Left));
    self.event_queue.push(SweepEndpoint(self.arena.back().unwrap(), SegmentEnd::Right));
    for event in &self.event_queue {
        println!("{:?}", event)

Aaand… this doesn’t work. Rust “cannot infer an appropriate lifetime for autoref due to conflicting requirements”. The trouble is that self.arena.back() takes a reference to self.arena, and then I put that reference in the event queue. But I promised that everything in the event queue has lifetime 'a, and I don’t actually know how long self lives here; I only know that it can’t outlive 'a, because that would invalidate the references it holds.

A little random guessing let me to change &mut self to &'a mut self — which is fine because the entire impl block this lives in is already parameterized by 'a — and that makes this compile! Hooray! I think that’s because I’m saying self itself has exactly the same lifetime as the references it holds onto, which is true, since it’s referring to itself.

Let’s get a little more ambitious and try having two segments.

fn run(&'a mut self) {
    self.arena.push_back(SweepSegment{ data: 5 });
    self.event_queue.push(SweepEndpoint(self.arena.back().unwrap(), SegmentEnd::Left));
    self.event_queue.push(SweepEndpoint(self.arena.back().unwrap(), SegmentEnd::Right));
    self.arena.push_back(SweepSegment{ data: 17 });
    self.event_queue.push(SweepEndpoint(self.arena.back().unwrap(), SegmentEnd::Left));
    self.event_queue.push(SweepEndpoint(self.arena.back().unwrap(), SegmentEnd::Right));
    for event in &self.event_queue {
        println!("{:?}", event)

Whoops! Rust complains that I’m trying to mutate self.arena while other stuff is referring to it. And, yes, that’s true — I have references to it in the event queue, and Rust is preventing me from potentially deleting everything from the queue when references to it still exist. I’m not actually deleting anything here, of course (though I could be if this were a Vec!), but Rust’s type system can’t encode that (and I dread the thought of a type system that can).

I struggled with this for a while, and rapidly encountered another complete showstopper:

fn run(&'a mut self) {

fn mutate_something(&'a mut self) {}

Rust objects that I’m trying to borrow self mutably, twice — once for the first call, once for the second.

But why? A borrow is supposed to end automatically once it’s no longer used, right? Maybe if I throw some braces around it for scope… nope, that doesn’t help either.

It’s true that borrows usually end automatically, but here I have explicitly told Rust that mutate_something() should borrow with the lifetime 'a, which is the same as the lifetime in run(). So the first call explicitly borrows self for at least the rest of the method. Removing the lifetime from mutate_something() does fix this error, but if that method tries to add new segments, I’m back to the original problem.

Oh no. The mutation in the C++ code is several calls deep. Porting it directly seems nearly impossible.

The typical solution here — at least, the first thing people suggest to me on Twitter — is to wrap basically everything everywhere in Rc<RefCell<T>>, which gives you something that’s reference-counted (avoiding questions of ownership) and defers borrow checks until runtime (avoiding questions of mutable borrows). But that seems pretty heavy-handed here — not only does RefCell add .borrow() noise anywhere you actually want to interact with the underlying value, but do I really need to refcount these tiny structs that only hold a handful of floats each?

I set out to find a middle ground.

Solution, kind of

I really, really didn’t want to perform serious surgery on this code just to get it to build. I still didn’t know if it worked at all, and now I had to rearrange it without being able to check if I was breaking it further. (This isn’t Rust’s fault; it’s a natural problem with porting between fairly different paradigms.)

So I kind of hacked it into working with minimal changes, producing a grotesque abomination which I’m ashamed to link to. Here’s how!

First, I got rid of the class. It turns out this makes lifetime juggling much easier right off the bat. I’m pretty sure Rust considers everything in a struct to be destroyed simultaneously (though in practice it guarantees it’ll destroy fields in order), which doesn’t leave much wiggle room. Locals within a function, on the other hand, can each have their own distinct lifetimes, which solves the problem of expressing that the borrows won’t outlive the arena.

Speaking of the arena, I solved the mutability problem there by switching to… an arena! The typed-arena crate (a port of a type used within Rust itself, I think) is an allocator — you give it a value, and it gives you back a reference, and the reference is guaranteed to be valid for as long as the arena exists. The method that does this is sneaky and takes &self rather than &mut self, so Rust doesn’t know you’re mutating the arena and won’t complain. (One drawback is that the arena will never free anything you give to it, but that’s not a big problem here.)

My next problem was with mutation. The main loop repeatedly calls possibleIntersection with pairs of segments, which can split either or both segment. Rust definitely doesn’t like that — I’d have to pass in two &muts, both of which are mutable references into the same arena, and I’d have a bunch of immutable references into that arena in the sweep list and elsewhere. This isn’t going to fly.

This is kind of a shame, and is one place where Rust seems a little overzealous. Something like this seems like it ought to be perfectly valid:

let mut v = vec![1u32, 2u32];
let a = &mut v[0];
let b = &mut v[1];
// do stuff with a, b

The trouble is, Rust only knows the type signature, which here is something like index_mut(&'a mut self, index: usize) -> &'a T. Nothing about that says that you’re borrowing distinct elements rather than some core part of the type — and, in fact, the above code is only safe because you’re borrowing distinct elements. In the general case, Rust can’t possibly know that. It seems obvious enough from the different indexes, but nothing about the type system even says that different indexes have to return different values. And what if one were borrowed as &mut v[1] and the other were borrowed with v.iter_mut().next().unwrap()?

Anyway, this is exactly where people start to turn to RefCell — if you’re very sure you know better than Rust, then a RefCell will skirt the borrow checker while still enforcing at runtime that you don’t have more than one mutable borrow at a time.

But half the lines in this algorithm examine the endpoints of a segment! I don’t want to wrap the whole thing in a RefCell, or I’ll have to say this everywhere:

if segment1.borrow().point.x < segment2.borrow().point.x { ... }


But wait — this code only mutates the points themselves in one place. When a segment is split, the original segment becomes the left half, and a new segment is created to be the right half. There’s no compelling need for this; it saves an allocation for the left half, but it’s not critical to the algorithm.

Thus, I settled on a compromise. My segment type now looks like this:

struct SegmentPacket {
    // a bunch of flags and whatnot used in the algorithm
struct SweepSegment {
    left_point: MapPoint,
    right_point: MapPoint,
    faces_outwards: bool,
    index: usize,
    order: usize,
    packet: RefCell<SegmentPacket>,

I do still need to call .borrow() or .borrow_mut() to get at the stuff in the “packet”, but that’s far less common, so there’s less noise overall. And I don’t need to wrap it in Rc because it’s part of a type that’s allocated in the arena and passed around only via references.

This still leaves me with the problem of how to actually perform the splits.

I’m not especially happy with what I came up with, I don’t know if I can defend it, and I suspect I could do much better. I changed possibleIntersection so that rather than performing splits, it returns the points at which each segment needs splitting, in the form (usize, Option<MapPoint>, Option<MapPoint>). (The usize is used as a flag for calling code and oughta be an enum, but, isn’t yet.)

Now the top-level function is responsible for all arena management, and all is well.

Except, er. possibleIntersection is called multiple times, and I don’t want to copy-paste a dozen lines of split code after each call. I tried putting just that code in its own function, which had the world’s most godawful signature, and that didn’t work because… uh… hm. I can’t remember why, exactly! Should’ve written that down.

I tried a local closure next, but closures capture their environment by reference, so now I had references to a bunch of locals for as long as the closure existed, which meant I couldn’t mutate those locals. Argh. (This seems a little silly to me, since the closure’s references cannot possibly be used for anything if the closure isn’t being called, but maybe I’m missing something. Or maybe this is just a limitation of lifetimes.)

Increasingly desperate, I tried using a macro. But… macros are hygienic, which means that any new name you use inside a macro is different from any name outside that macro. The macro thus could not see any of my locals. Usually that’s good, but here I explicitly wanted the macro to mess with my locals.

I was just about to give up and go live as a hermit in a cabin in the woods, when I discovered something quite incredible. You can define local macros! If you define a macro inside a function, then it can see any locals defined earlier in that function. Perfect!

macro_rules! _split_segment (
    ($seg:expr, $pt:expr) => (
            let pt = $pt;
            let seg = $seg;
            // ... waaay too much code ...

loop {
    // ...
    // This is possibleIntersection, renamed because Rust rightfully complains about camelCase
    let cross = handle_intersections(Some(segment), maybe_above);
    if let Some(pt) = cross.1 {
        segment = _split_segment!(segment, pt);
    if let Some(pt) = cross.2 {
        maybe_above = Some(_split_segment!(maybe_above.unwrap(), pt));
    // ...

(This doesn’t actually quite match the original algorithm, which has one case where a segment can be split twice. I realized that I could just do the left-most split, and a later iteration would perform the other split. I sure hope that’s right, anyway.)

It’s a bit ugly, and I ran into a whole lot of implicit behavior from the C++ code that I had to fix — for example, the segment is sometimes mutated just before it’s split, purely as a shortcut for mutating the left part of the split. But it finally compiles! And runs! And kinda worked, a bit!


I still had a lot of work to do.

For one, this code was designed for intersecting two shapes, not mass-intersecting a big pile of shapes. The basic algorithm doesn’t care about how many polygons you start with — all it sees is segments — but the code for constructing the return value needed some heavy modification.

The biggest change by far? The original code traced each segment once, expecting the result to be only a single shape. I had to change that to trace each side of each segment once, since the vast bulk of the output consists of shapes which share a side. This violated a few assumptions, which I had to hack around.

I also ran into a couple very bad edge cases, spent ages debugging them, then found out that the original algorithm had a subtle workaround that I’d commented out because it was awkward to port but didn’t seem to do anything. Whoops!

The worst was a precision error, where a vertical line could be split on a point not quite actually on the line, which wreaked all kinds of havoc. I worked around that with some tasteful rounding, which is highly dubious but makes the output more appealing to my squishy human brain. (I might switch to the original workaround, but I really dislike that even simple cases can spit out points at 1500.0000000000003. The whole thing is parameterized over the coordinate type, so maybe I could throw a rational type in there and cross my fingers?)

All that done, I finally, finally, after a couple months of intermittent progress, got what I wanted!

This is Doom 2’s MAP01. The black area to the left of center is where the player starts. Gray areas indicate where the player can walk from there, with lighter shades indicating more distant areas, where “distance” is measured by the minimum number of line crossings. Red areas can’t be reached at all.

(Note: large playable chunks of the map, including the exit room, are red. That’s because those areas are behind doors, and this code doesn’t understand doors yet.)

(Also note: The big crescent in the lower-right is also black because I was lazy and looked for the player’s starting sector by checking the bbox, and that sector’s bbox happens to match.)

The code that generated this had to go out of its way to delete all the unreachable zones around solid walls. I think I could modify the algorithm to do that on the fly pretty easily, which would probably speed it up a bit too. Downside is that the algorithm would then be pretty specifically tied to this problem, and not usable for any other kind of polygon intersection, which I would think could come up elsewhere? The modifications would be pretty minor, though, so maybe I could confine them to a closure or something.

Some final observations

It runs surprisingly slowly. Like, multiple seconds. Unless I add --release, which speeds it up by a factor of… some number with multiple digits. Wahoo. Debug mode has a high price, especially with a lot of calls in play.

The current state of this code is on GitHub. Please don’t look at it. I’m very sorry.

Honestly, most of my anguish came not from Rust, but from the original code relying on lots of fairly subtle behavior without bothering to explain what it was doing or even hint that anything unusual was going on. God, I hate C++.

I don’t know if the Rust community can learn from this. I don’t know if I even learned from this. Let’s all just quietly forget about it.

Now I just need to figure this one out…

WannaCry after one year

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/03/wannacry-after-one-year.html

In the news, Boeing (an aircraft maker) has been “targeted by a WannaCry virus attack”. Phrased this way, it’s implausible. There are no new attacks targeting people with WannaCry. There is either no WannaCry, or it’s simply a continuation of the attack from a year ago.

It’s possible what happened is that an anti-virus product called a new virus “WannaCry”. Virus families are often related, and sometimes a distant relative gets called the same thing. I know this watching the way various anti-virus products label my own software, which isn’t a virus, but which virus writers often include with their own stuff. The Lazarus group, which is believed to be responsible for WannaCry, have whole virus families like this. Thus, just because an AV product claims you are infected with WannaCry doesn’t mean it’s the same thing that everyone else is calling WannaCry.

Famously, WannaCry was the first virus/ransomware/worm that used the NSA ETERNALBLUE exploit. Other viruses have since added the exploit, and of course, hackers use it when attacking systems. It may be that a network intrusion detection system detected ETERNALBLUE, which people then assumed was due to WannaCry. It may actually have been an nPetya infection instead (nPetya was the second major virus/worm/ransomware to use the exploit).

Or it could be the real WannaCry, but it’s probably not a new “attack” that “targets” Boeing. Instead, it’s likely a continuation from WannaCry’s first appearance. WannaCry is a worm, which means it spreads automatically after it was launched, for years, without anybody in control. Infected machines still exist, unnoticed by their owners, attacking random machines on the Internet. If you plug in an unpatched computer onto the raw Internet, without the benefit of a firewall, it’ll get infected within an hour.

However, the Boeing manufacturing systems that were infected were not on the Internet, so what happened? The narrative from the news stories imply some nefarious hacker activity that “targeted” Boeing, but that’s unlikely.

We have now have over 15 years of experience with network worms getting into strange places disconnected and even “air gapped” from the Internet. The most common reason is laptops. Somebody takes their laptop to some place like an airport WiFi network, and gets infected. They put their laptop to sleep, then wake it again when they reach their destination, and plug it into the manufacturing network. At this point, the virus spreads and infects everything. This is especially the case with maintenance/support engineers, who often have specialized software they use to control manufacturing machines, for which they have a reason to connect to the local network even if it doesn’t have useful access to the Internet. A single engineer may act as a sort of Typhoid Mary, going from customer to customer, infecting each in turn whenever they open their laptop.

Another cause for infection is virtual machines. A common practice is to take “snapshots” of live machines and save them to backups. Should the virtual machine crash, instead of rebooting it, it’s simply restored from the backed up running image. If that backup image is infected, then bringing it out of sleep will allow the worm to start spreading.

Jake Williams claims he’s seen three other manufacturing networks infected with WannaCry. Why does manufacturing seem more susceptible? The reason appears to be the “killswitch” that stops WannaCry from running elsewhere. The killswitch uses a DNS lookup, stopping itself if it can resolve a certain domain. Manufacturing networks are largely disconnected from the Internet enough that such DNS lookups don’t work, so the domain can’t be found, so the killswitch doesn’t work. Thus, manufacturing systems are no more likely to get infected, but the lack of killswitch means the virus will continue to run, attacking more systems instead of immediately killing itself.

One solution to this would be to setup sinkhole DNS servers on the network that resolve all unknown DNS queries to a single server that logs all requests. This is trivially setup with most DNS servers. The logs will quickly identify problems on the network, as well as any hacker or virus activity. The side effect is that it would make this killswitch kill WannaCry. WannaCry isn’t sufficient reason to setup sinkhole servers, of course, but it’s something I’ve found generally useful in the past.


Something obviously happened to the Boeing plant, but the narrative is all wrong. Words like “targeted attack” imply things that likely didn’t happen. Facts are so loose in cybersecurity that it may not have even been WannaCry.

The real story is that the original WannaCry is still out there, still trying to spread. Simply put a computer on the raw Internet (without a firewall) and you’ll get attacked. That, somehow, isn’t news. Instead, what’s news is whenever that continued infection hits somewhere famous, like Boeing, even though (as Boeing claims) it had no important effect.

Facebook and Cambridge Analytica

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/03/facebook_and_ca.html

In the wake of the Cambridge Analytica scandal, news articles and commentators have focused on what Facebook knows about us. A lot, it turns out. It collects data from our posts, our likes, our photos, things we type and delete without posting, and things we do while not on Facebook and even when we’re offline. It buys data about us from others. And it can infer even more: our sexual orientation, political beliefs, relationship status, drug use, and other personality traits — even if we didn’t take the personality test that Cambridge Analytica developed.

But for every article about Facebook’s creepy stalker behavior, thousands of other companies are breathing a collective sigh of relief that it’s Facebook and not them in the spotlight. Because while Facebook is one of the biggest players in this space, there are thousands of other companies that spy on and manipulate us for profit.

Harvard Business School professor Shoshana Zuboff calls it “surveillance capitalism.” And as creepy as Facebook is turning out to be, the entire industry is far creepier. It has existed in secret far too long, and it’s up to lawmakers to force these companies into the public spotlight, where we can all decide if this is how we want society to operate and — if not — what to do about it.

There are 2,500 to 4,000 data brokers in the United States whose business is buying and selling our personal data. Last year, Equifax was in the news when hackers stole personal information on 150 million people, including Social Security numbers, birth dates, addresses, and driver’s license numbers.

You certainly didn’t give it permission to collect any of that information. Equifax is one of those thousands of data brokers, most of them you’ve never heard of, selling your personal information without your knowledge or consent to pretty much anyone who will pay for it.

Surveillance capitalism takes this one step further. Companies like Facebook and Google offer you free services in exchange for your data. Google’s surveillance isn’t in the news, but it’s startlingly intimate. We never lie to our search engines. Our interests and curiosities, hopes and fears, desires and sexual proclivities, are all collected and saved. Add to that the websites we visit that Google tracks through its advertising network, our Gmail accounts, our movements via Google Maps, and what it can collect from our smartphones.

That phone is probably the most intimate surveillance device ever invented. It tracks our location continuously, so it knows where we live, where we work, and where we spend our time. It’s the first and last thing we check in a day, so it knows when we wake up and when we go to sleep. We all have one, so it knows who we sleep with. Uber used just some of that information to detect one-night stands; your smartphone provider and any app you allow to collect location data knows a lot more.

Surveillance capitalism drives much of the internet. It’s behind most of the “free” services, and many of the paid ones as well. Its goal is psychological manipulation, in the form of personalized advertising to persuade you to buy something or do something, like vote for a candidate. And while the individualized profile-driven manipulation exposed by Cambridge Analytica feels abhorrent, it’s really no different from what every company wants in the end. This is why all your personal information is collected, and this is why it is so valuable. Companies that can understand it can use it against you.

None of this is new. The media has been reporting on surveillance capitalism for years. In 2015, I wrote a book about it. Back in 2010, the Wall Street Journal published an award-winning two-year series about how people are tracked both online and offline, titled “What They Know.”

Surveillance capitalism is deeply embedded in our increasingly computerized society, and if the extent of it came to light there would be broad demands for limits and regulation. But because this industry can largely operate in secret, only occasionally exposed after a data breach or investigative report, we remain mostly ignorant of its reach.

This might change soon. In 2016, the European Union passed the comprehensive General Data Protection Regulation, or GDPR. The details of the law are far too complex to explain here, but some of the things it mandates are that personal data of EU citizens can only be collected and saved for “specific, explicit, and legitimate purposes,” and only with explicit consent of the user. Consent can’t be buried in the terms and conditions, nor can it be assumed unless the user opts in. This law will take effect in May, and companies worldwide are bracing for its enforcement.

Because pretty much all surveillance capitalism companies collect data on Europeans, this will expose the industry like nothing else. Here’s just one example. In preparation for this law, PayPal quietly published a list of over 600 companies it might share your personal data with. What will it be like when every company has to publish this sort of information, and explicitly explain how it’s using your personal data? We’re about to find out.

In the wake of this scandal, even Mark Zuckerberg said that his industry probably should be regulated, although he’s certainly not wishing for the sorts of comprehensive regulation the GDPR is bringing to Europe.

He’s right. Surveillance capitalism has operated without constraints for far too long. And advances in both big data analysis and artificial intelligence will make tomorrow’s applications far creepier than today’s. Regulation is the only answer.

The first step to any regulation is transparency. Who has our data? Is it accurate? What are they doing with it? Who are they selling it to? How are they securing it? Can we delete it? I don’t see any hope of Congress passing a GDPR-like data protection law anytime soon, but it’s not too far-fetched to demand laws requiring these companies to be more transparent in what they’re doing.

One of the responses to the Cambridge Analytica scandal is that people are deleting their Facebook accounts. It’s hard to do right, and doesn’t do anything about the data that Facebook collects about people who don’t use Facebook. But it’s a start. The market can put pressure on these companies to reduce their spying on us, but it can only do that if we force the industry out of its secret shadows.

This essay previously appeared on CNN.com.

EDITED TO ADD (4/2): Slashdot thread.

World Backup Day 2018: Backing Up The World

Post Syndicated from Yev original https://www.backblaze.com/blog/world-backup-day-2018-backing-up-the-world/

World Backup Day is March 31st, 2018. The tagline is usually something along the lines of: “Back up! Don’t be an April Fool.” This year we don’t have any gimmicks or promotions for World Backup Day, but we do want to share something with you.

Countries with Backblaze Customers

Countries with Backblaze Customers

That is a world map of every country where Backblaze is backing up someone’s data. To save you some counting, that’s over 150 countries where people have peace of mind using Backblaze. If you’re not already backing up, or know people who haven’t started backing up their computers yet, we invite you to join the rest of the world on this World Backup Day and start backing up with Backblaze! At only $50/year for unlimited data backup of your PC or Mac, it’s time to get started with Backblaze.

It’s great that World Backup Day is around to remind everyone that it’s important to back up your data, especially in the wake of ransomware attacks like the most recent SamSam virus (we wrote a complete guide to recovering from ransomware should something like this happen to you).

At Backblaze, we believe that every day is backup day. That’s why Backblaze Cloud Backup installs in seconds and starts immediately backing up everything on your computer, with no limit on how much data you have. That gives you peace of mind on World Backup Day and every other day of the year.

If you know people who could use that peace of mind, refer them to: Have Friends Who Don’t Back Up? Share This Post! That will help them get started!

The post World Backup Day 2018: Backing Up The World appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Alex’s quick and easy digital making Easter egg hunt

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/alexs-easter-egg-hunt/

Looking to incorporate some digital making into your Easter weekend? You’ve come to the right place! With a Raspberry Pi, a few wires, and some simple code, you can take your festivities to the next level — here’s how!

Easter Egg Hunt using Raspberry Pi

If you logged in to watch our Instagram live-stream yesterday, you’ll have seen me put together a simple egg carton and some wires to create circuits. These circuits, when closed by way of a foil-wrapped chocolate egg, instruct a Raspberry Pi to reveal the whereabouts of a larger chocolate egg!

Make it

You’ll need an egg carton, two male-to-female jumper wire, and two crocodile leads for each egg you use.

Easter Egg Hunt using Raspberry Pi

Connect your leads together in pairs: one end of a crocodile lead to the male end of one jumper wire. Attach the free crocodile clips of two leads to each corner of the egg carton (as shown up top). Then hook up the female ends to GPIO pins: one numbered pin and one ground pin per egg. I recommend pins 3, 4, 18 and 24, as they all have adjacent GND pins.

Easter Egg Hunt using Raspberry Pi

Your foil-wrapped Easter egg will complete the circuit — make sure it’s touching both the GPIO- and GND-connected clips when resting in the carton.

Easter Egg Hunt using Raspberry Pi

Wrap it

For your convenience (and our sweet tooth), we tested several foil-wrapped eggs (Easter and otherwise) to see which are conductive.

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We’re egg-sperimenting with Easter deliciousness to find which treat is the most conductive. Why? All will be revealed in our Instagram Easter live-stream tomorrow.

The result? None of them are! But if you unwrap an egg and rewrap it with the non-decorative foil side outward, this tends to work. You could also use aluminium foil or copper tape to create a conductive layer.

Code it

Next, you’ll need to create the code for your hunt. The script below contains the bare bones needed to make the project work — you can embellish it however you wish using GUIs, flashing LEDs, music, etc.

Open Thonny or IDLE on Raspbian and create a new file called egghunt.py. Then enter the following code:

We’re using ButtonBoard from the gpiozero library. This allows us to link several buttons together as an object and set an action for when any number of the buttons are pressed. Here, the script waits for all four circuits to be completed before printing the location of the prize in the Python shell.

Your turn

And that’s it! Now you just need to hide your small foil eggs around the house and challenge your kids/friends/neighbours to find them. Then, once every circuit is completed with an egg, the great prize will be revealed.

Give it a go this weekend! And if you do, be sure to let us know on social media.

(Thank you to Lauren Hyams for suggesting we “do something for Easter” and Ben ‘gpiozero’ Nuttall for introducing me to ButtonBoard.)

The post Alex’s quick and easy digital making Easter egg hunt appeared first on Raspberry Pi.

The robotic teapot from your nightmares

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/robotic-teapot/

For those moments when you wish the cast of Disney’s Beauty and the Beast was real, only to realise what a nightmare that would be, here’s Paul-Louis Ageneau’s robotic teapot!

Paul-Louis Ageneau Robotic teapot Raspberry Pi Zero

See what I mean?

Tale as old as time…

It’s the classic story of guy meets digital killer teapot, digital killer teapot inspires him to 3D print his own. Loosely based on a boss level of the video game Alice: Madness Returns, Paul-Louis’s creation is a one-eyed walking teapot robot with a (possible) thirst for blood.

Kill Build the beast

“My new robot is based on a Raspberry Pi Zero W with a camera.” Paul-Louis explains in his blog. “It is connected via a serial link to an Arduino Pro Mini board, which drives servos.”

Each leg has two points of articulation, one for the knee and one for the ankle. In order to move each of the joints, the teapot uses eight servo motor in total.

Paul-Louis Ageneau Robotic teapot Raspberry Pi Zero

Paul-Louis designed and 3D printed the body of the teapot to fit the components needed. So if you’re considering this build as a means of acquiring tea on your laziest of days, I hate to be the bearer of bad news, but the most you’ll get from your pour will be jumper leads and Pi.

Paul-Louis Ageneau Robotic Raspberry Pi Zero teapot
Paul-Louis Ageneau Robotic Raspberry Pi Zero teapot
Paul-Louis Ageneau Robotic Raspberry Pi Zero teapot

While the Arduino board controls the legs, it’s the Raspberry Pi’s job to receive user commands and tell the board how to direct the servos. The protocol for moving the servos is simple, with short lines of characters specifying instructions. First a digit from 0 to 7 selects a servo; next the angle of movement, such as 45 or 90, is input; and finally, the use of C commits the instruction.

Typing in commands is great for debugging, but you don’t want to be glued to a keyboard. Therefore, Paul-Louis continued to work on the code in order to string together several lines to create larger movements.

Paul-Louis Ageneau Robotic teapot Raspberry Pi Zero

The final control system of the teapot runs on a web browser as a standard four-axis arrow pad, with two extra arrows for turning.

Something there that wasn’t there before

Jean-Paul also included an ‘eye’ in the side of the pot to fit the Raspberry Pi Camera Module as another nod to the walking teapot from the video game, but with a purpose other than evil and wrong-doing. As you can see from the image above, the camera live-streams footage, allowing for remote control of the monster teapot regardless of your location.

If you like it all that much, it’s yours

In case you fancy yourself as an inventor, Paul-Louis has provided the entire build process and the code on his blog, documenting how to bring your own teapot to life. And if you’ve created any robotic household items or any props from video games or movies, we’d love to see them, so leave a link in the comments or share it with us across social media using the hashtag #IBuiltThisAndNowIThinkItIsTryingToKillMe.

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