Tag Archives: Technical

AWS GDPR Data Processing Addendum – Now Part of Service Terms

Post Syndicated from Chad Woolf original https://aws.amazon.com/blogs/security/aws-gdpr-data-processing-addendum/

Today, we’re happy to announce that the AWS GDPR Data Processing Addendum (GDPR DPA) is now part of our online Service Terms. This means all AWS customers globally can rely on the terms of the AWS GDPR DPA which will apply automatically from May 25, 2018, whenever they use AWS services to process personal data under the GDPR. The AWS GDPR DPA also includes EU Model Clauses, which were approved by the European Union (EU) data protection authorities, known as the Article 29 Working Party. This means that AWS customers wishing to transfer personal data from the European Economic Area (EEA) to other countries can do so with the knowledge that their personal data on AWS will be given the same high level of protection it receives in the EEA.

As we approach the GDPR enforcement date this week, this announcement is an important GDPR compliance component for us, our customers, and our partners. All customers which that are using cloud services to process personal data will need to have a data processing agreement in place between them and their cloud services provider if they are to comply with GDPR. As early as April 2017, AWS announced that AWS had a GDPR-ready DPA available for its customers. In this way, we started offering our GDPR DPA to customers over a year before the May 25, 2018 enforcement date. Now, with the DPA terms included in our online service terms, there is no extra engagement needed by our customers and partners to be compliant with the GDPR requirement for data processing terms.

The AWS GDPR DPA also provides our customers with a number of other important assurances, such as the following:

  • AWS will process customer data only in accordance with customer instructions.
  • AWS has implemented and will maintain robust technical and organizational measures for the AWS network.
  • AWS will notify its customers of a security incident without undue delay after becoming aware of the security incident.
  • AWS will make available certificates issued in relation to the ISO 27001 certification, the ISO 27017 certification, and the ISO 27018 certification to further help customers and partners in their own GDPR compliance activities.

Customers who have already signed an offline version of the AWS GDPR DPA can continue to rely on that GDPR DPA. By incorporating our GDPR DPA into the AWS Service Terms, we are simply extending the terms of our GDPR DPA to all customers globally who will require it under GDPR.

AWS GDPR DPA is only part of the story, however. We are continuing to work alongside our customers and partners to help them on their journey towards GDPR compliance.

If you have any questions about the GDPR or the AWS GDPR DPA, please contact your account representative, or visit the AWS GDPR Center at: https://aws.amazon.com/compliance/gdpr-center/

-Chad

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Introducing the AWS Machine Learning Competency for Consulting Partners

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/introducing-the-aws-machine-learning-competency-for-consulting-partners/

Today I’m excited to announce a new Machine Learning Competency for Consulting Partners in the Amazon Partner Network (APN). This AWS Competency program allows APN Consulting Partners to demonstrate a deep expertise in machine learning on AWS by providing solutions that enable machine learning and data science workflows for their customers. This new AWS Competency is in addition to the Machine Learning comptency for our APN Technology Partners, that we launched at the re:Invent 2017 partner summit.

These APN Consulting Partners help organizations solve their machine learning and data challenges through:

  • Providing data services that help data scientists and machine learning practitioners prepare their enterprise data for training.
  • Platform solutions that provide data scientists and machine learning practitioners with tools to take their data, train models, and make predictions on new data.
  • SaaS and API solutions to enable predictive capabilities within customer applications.

Why work with an AWS Machine Learning Competency Partner?

The AWS Competency Program helps customers find the most qualified partners with deep expertise. AWS Machine Learning Competency Partners undergo a strict validation of their capabilities to demonstrate technical proficiency and proven customer success with AWS machine learning tools.

If you’re an AWS customer interested in machine learning workloads on AWS, check out our AWS Machine Learning launch partners below:

 

Interested in becoming an AWS Machine Learning Competency Partner?

APN Partners with experience in Machine Learning can learn more about becoming an AWS Machine Learning Competency Partner here. To learn more about the benefits of joining the AWS Partner Network, see our APN Partner website.

Thanks to the AWS Partner Team for their help with this post!
Randall

AWS Online Tech Talks – May and Early June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-may-and-early-june-2018/

AWS Online Tech Talks – May and Early June 2018  

Join us this month to learn about some of the exciting new services and solution best practices at AWS. We also have our first re:Invent 2018 webinar series, “How to re:Invent”. Sign up now to learn more, we look forward to seeing you.

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

Analytics & Big Data

May 21, 2018 | 11:00 AM – 11:45 AM PT Integrating Amazon Elasticsearch with your DevOps Tooling – Learn how you can easily integrate Amazon Elasticsearch Service into your DevOps tooling and gain valuable insight from your log data.

May 23, 2018 | 11:00 AM – 11:45 AM PTData Warehousing and Data Lake Analytics, Together – Learn how to query data across your data warehouse and data lake without moving data.

May 24, 2018 | 11:00 AM – 11:45 AM PTData Transformation Patterns in AWS – Discover how to perform common data transformations on the AWS Data Lake.

Compute

May 29, 2018 | 01:00 PM – 01:45 PM PT – Creating and Managing a WordPress Website with Amazon Lightsail – Learn about Amazon Lightsail and how you can create, run and manage your WordPress websites with Amazon’s simple compute platform.

May 30, 2018 | 01:00 PM – 01:45 PM PTAccelerating Life Sciences with HPC on AWS – Learn how you can accelerate your Life Sciences research workloads by harnessing the power of high performance computing on AWS.

Containers

May 24, 2018 | 01:00 PM – 01:45 PM PT – Building Microservices with the 12 Factor App Pattern on AWS – Learn best practices for building containerized microservices on AWS, and how traditional software design patterns evolve in the context of containers.

Databases

May 21, 2018 | 01:00 PM – 01:45 PM PTHow to Migrate from Cassandra to Amazon DynamoDB – Get the benefits, best practices and guides on how to migrate your Cassandra databases to Amazon DynamoDB.

May 23, 2018 | 01:00 PM – 01:45 PM PT5 Hacks for Optimizing MySQL in the Cloud – Learn how to optimize your MySQL databases for high availability, performance, and disaster resilience using RDS.

DevOps

May 23, 2018 | 09:00 AM – 09:45 AM PT.NET Serverless Development on AWS – Learn how to build a modern serverless application in .NET Core 2.0.

Enterprise & Hybrid

May 22, 2018 | 11:00 AM – 11:45 AM PTHybrid Cloud Customer Use Cases on AWS – Learn how customers are leveraging AWS hybrid cloud capabilities to easily extend their datacenter capacity, deliver new services and applications, and ensure business continuity and disaster recovery.

IoT

May 31, 2018 | 11:00 AM – 11:45 AM PTUsing AWS IoT for Industrial Applications – Discover how you can quickly onboard your fleet of connected devices, keep them secure, and build predictive analytics with AWS IoT.

Machine Learning

May 22, 2018 | 09:00 AM – 09:45 AM PTUsing Apache Spark with Amazon SageMaker – Discover how to use Apache Spark with Amazon SageMaker for training jobs and application integration.

May 24, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS DeepLens – Learn how AWS DeepLens provides a new way for developers to learn machine learning by pairing the physical device with a broad set of tutorials, examples, source code, and integration with familiar AWS services.

Management Tools

May 21, 2018 | 09:00 AM – 09:45 AM PTGaining Better Observability of Your VMs with Amazon CloudWatch – Learn how CloudWatch Agent makes it easy for customers like Rackspace to monitor their VMs.

Mobile

May 29, 2018 | 11:00 AM – 11:45 AM PT – Deep Dive on Amazon Pinpoint Segmentation and Endpoint Management – See how segmentation and endpoint management with Amazon Pinpoint can help you target the right audience.

Networking

May 31, 2018 | 09:00 AM – 09:45 AM PTMaking Private Connectivity the New Norm via AWS PrivateLink – See how PrivateLink enables service owners to offer private endpoints to customers outside their company.

Security, Identity, & Compliance

May 30, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Certificate Manager Private Certificate Authority (CA) – Learn how AWS Certificate Manager (ACM) Private Certificate Authority (CA), a managed private CA service, helps you easily and securely manage the lifecycle of your private certificates.

June 1, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS Firewall Manager – Centrally configure and manage AWS WAF rules across your accounts and applications.

Serverless

May 22, 2018 | 01:00 PM – 01:45 PM PTBuilding API-Driven Microservices with Amazon API Gateway – Learn how to build a secure, scalable API for your application in our tech talk about API-driven microservices.

Storage

May 30, 2018 | 11:00 AM – 11:45 AM PTAccelerate Productivity by Computing at the Edge – Learn how AWS Snowball Edge support for compute instances helps accelerate data transfers, execute custom applications, and reduce overall storage costs.

June 1, 2018 | 11:00 AM – 11:45 AM PTLearn to Build a Cloud-Scale Website Powered by Amazon EFS – Technical deep dive where you’ll learn tips and tricks for integrating WordPress, Drupal and Magento with Amazon EFS.

 

 

 

 

Ray Ozzie’s Encryption Backdoor

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/ray_ozzies_encr.html

Last month, Wired published a long article about Ray Ozzie and his supposed new scheme for adding a backdoor in encrypted devices. It’s a weird article. It paints Ozzie’s proposal as something that “attains the impossible” and “satisfies both law enforcement and privacy purists,” when (1) it’s barely a proposal, and (2) it’s essentially the same key escrow scheme we’ve been hearing about for decades.

Basically, each device has a unique public/private key pair and a secure processor. The public key goes into the processor and the device, and is used to encrypt whatever user key encrypts the data. The private key is stored in a secure database, available to law enforcement on demand. The only other trick is that for law enforcement to use that key, they have to put the device in some sort of irreversible recovery mode, which means it can never be used again. That’s basically it.

I have no idea why anyone is talking as if this were anything new. Several cryptographers have already explained why this key escrow scheme is no better than any other key escrow scheme. The short answer is (1) we won’t be able to secure that database of backdoor keys, (2) we don’t know how to build the secure coprocessor the scheme requires, and (3) it solves none of the policy problems around the whole system. This is the typical mistake non-cryptographers make when they approach this problem: they think that the hard part is the cryptography to create the backdoor. That’s actually the easy part. The hard part is ensuring that it’s only used by the good guys, and there’s nothing in Ozzie’s proposal that addresses any of that.

I worry that this kind of thing is damaging in the long run. There should be some rule that any backdoor or key escrow proposal be a fully specified proposal, not just some cryptography and hand-waving notions about how it will be used in practice. And before it is analyzed and debated, it should have to satisfy some sort of basic security analysis. Otherwise, we’ll be swatting pseudo-proposals like this one, while those on the other side of this debate become increasingly convinced that it’s possible to design one of these things securely.

Already people are using the National Academies report on backdoors for law enforcement as evidence that engineers are developing workable and secure backdoors. Writing in Lawfare, Alan Z. Rozenshtein claims that the report — and a related New York Times story — “undermine the argument that secure third-party access systems are so implausible that it’s not even worth trying to develop them.” Susan Landau effectively corrects this misconception, but the damage is done.

Here’s the thing: it’s not hard to design and build a backdoor. What’s hard is building the systems — both technical and procedural — around them. Here’s Rob Graham:

He’s only solving the part we already know how to solve. He’s deliberately ignoring the stuff we don’t know how to solve. We know how to make backdoors, we just don’t know how to secure them.

A bunch of us cryptographers have already explained why we don’t think this sort of thing will work in the foreseeable future. We write:

Exceptional access would force Internet system developers to reverse “forward secrecy” design practices that seek to minimize the impact on user privacy when systems are breached. The complexity of today’s Internet environment, with millions of apps and globally connected services, means that new law enforcement requirements are likely to introduce unanticipated, hard to detect security flaws. Beyond these and other technical vulnerabilities, the prospect of globally deployed exceptional access systems raises difficult problems about how such an environment would be governed and how to ensure that such systems would respect human rights and the rule of law.

Finally, Matthew Green:

The reason so few of us are willing to bet on massive-scale key escrow systems is that we’ve thought about it and we don’t think it will work. We’ve looked at the threat model, the usage model, and the quality of hardware and software that exists today. Our informed opinion is that there’s no detection system for key theft, there’s no renewability system, HSMs are terrifically vulnerable (and the companies largely staffed with ex-intelligence employees), and insiders can be suborned. We’re not going to put the data of a few billion people on the line an environment where we believe with high probability that the system will fail.

EDITED TO ADD (5/14): An analysis of the proposal.

Bad Software Is Our Fault

Post Syndicated from Bozho original https://techblog.bozho.net/bad-software-is-our-fault/

Bad software is everywhere. One can even claim that every software is bad. Cool companies, tech giants, established companies, all produce bad software. And no, yours is not an exception.

Who’s to blame for bad software? It’s all complicated and many factors are intertwined – there’s business requirements, there’s organizational context, there’s lack of sufficient skilled developers, there’s the inherent complexity of software development, there’s leaky abstractions, reliance on 3rd party software, consequences of wrong business and purchase decisions, time limitations, flawed business analysis, etc. So yes, despite the catchy title, I’m aware it’s actually complicated.

But in every “it’s complicated” scenario, there’s always one or two factors that are decisive. All of them contribute somehow, but the major drivers are usually a handful of things. And in the case of base software, I think it’s the fault of technical people. Developers, architects, ops.

We don’t seem to care about best practices. And I’ll do some nasty generalizations here, but bear with me. We can spend hours arguing about tabs vs spaces, curly bracket on new line, git merge vs rebase, which IDE is better, which framework is better and other largely irrelevant stuff. But we tend to ignore the important aspects that span beyond the code itself. The context in which the code lives, the non-functional requirements – robustness, security, resilience, etc.

We don’t seem to get security. Even trivial stuff such as user authentication is almost always implemented wrong. These days Twitter and GitHub realized they have been logging plain-text passwords, for example, but that’s just the tip of the iceberg. Too often we ignore the security implications.

“But the business didn’t request the security features”, one may say. The business never requested 2-factor authentication, encryption at rest, PKI, secure (or any) audit trail, log masking, crypto shredding, etc., etc. Because the business doesn’t know these things – we do and we have to put them on the backlog and fight for them to be implemented. Each organization has its specifics and tech people can influence the backlog in different ways, but almost everywhere we can put things there and prioritize them.

The other aspect is testing. We should all be well aware by now that automated testing is mandatory. We have all the tools in the world for unit, functional, integration, performance and whatnot testing, and yet many software projects lack the necessary test coverage to be able to change stuff without accidentally breaking things. “But testing takes time, we don’t have it”. We are perfectly aware that testing saves time, as we’ve all had those “not again!” recurring bugs. And yet we think of all sorts of excuses – “let the QAs test it”, we have to ship that now, we’ll test it later”, “this is too trivial to be tested”, etc.

And you may say it’s not our job. We don’t define what has do be done, we just do it. We don’t define the budget, the scope, the features. We just write whatever has been decided. And that’s plain wrong. It’s not our job to make money out of our code, and it’s not our job to define what customers need, but apart from that everything is our job. The way the software is structured, the security aspects and security features, the stability of the code base, the way the software behaves in different environments. The non-functional requirements are our job, and putting them on the backlog is our job.

You’ve probably heard that every software becomes “legacy” after 6 months. And that’s because of us, our sloppiness, our inability to mitigate external factors and constraints. Too often we create a mess through “just doing our job”.

And of course that’s a generalization. I happen to know a lot of great professionals who don’t make these mistakes, who strive for excellence and implement things the right way. But our industry as a whole doesn’t. Our industry as a whole produces bad software. And it’s our fault, as developers – as the only people who know why a certain piece of software is bad.

In a talk of his, Bob Martin warns us of the risks of our sloppiness. We have been building websites so far, but we are more and more building stuff that interacts with the real world, directly and indirectly. Ultimately, lives may depend on our software (like the recent unfortunate death caused by a self-driving car). And I’ll agree with Uncle Bob that it’s high time we self-regulate as an industry, before some technically incompetent politician decides to do that.

How, I don’t know. We’ll have to think more about it. But I’m pretty sure it’s our fault that software is bad, and no amount of blaming the management, the budget, the timing, the tools or the process can eliminate our responsibility.

Why do I insist on bashing my fellow software engineers? Because if we start looking at software development with more responsibility; with the fact that if it fails, it’s our fault, then we’re more likely to get out of our current bug-ridden, security-flawed, fragile software hole and really become the experts of the future.

The post Bad Software Is Our Fault appeared first on Bozho's tech blog.

3D-printed speakers from the Technical University of Denmark

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/technical-university-denmark-speakers/

Students taking Design of Mechatronics at the Technical University of Denmark have created some seriously elegant and striking Raspberry Pi speakers. Their builds are part of a project asking them to “explore, design and build a 3D printed speaker, around readily available electronics and components”.

The students have been uploading their designs, incorporating Raspberry Pis and HiFiBerry HATs, to Thingiverse throughout April. The task is a collaboration with luxury brand Bang & Olufsen’s Create initiative, and the results wouldn’t look out of place in a high-end showroom; I’d happily take any of these home.

The Sphere

Søren Qvist Sphere 3D-printed laser-cut Raspberry Pi Speaker
Søren Qvist Sphere 3D-printed laser-cut Raspberry Pi Speaker
Søren Qvist Sphere 3D-printed laser-cut Raspberry Pi Speaker

Søren Qvist’s wall-mounted kitchen sphere uses 3D-printed and laser-cut parts, along with the HiFiBerry HAT and B&O speakers to create a sleek-looking design.

Hex One

Otto Ømann Hex One 3D-printed laser-cut Raspberry Pi Speaker

Otto Ømann Hex One 3D-printed laser-cut Raspberry Pi Speaker

Otto Ømann’s group have designed the Hex One – a work-in-progress wireless 360° speaker. A particular objective for their project is to create a speaker using as many 3D-printed parts as possible.

Portable B&O-Create Speaker



“The design is supposed to resemble that of a B&O speaker, and from a handful of categories we chose to create a portable and wearable speaker,” explain Gustav Larsen and his team.

Desktop Loudspeaker

Oliver Repholtz Behrens loudspeaker

Oliver Repholtz Behrens loudspeaker

Oliver Repholtz Behrens and team have housed a Raspberry Pi and HiFiBerry HAT inside this this stylish airplay speaker. You can follow their design progress on their team blog.

B&O TILE



Tue Thomsen’s six-person team Mechatastic have produced the B&O TILE. “The speaker consists of four 3D-printed cabinet and top parts, where the top should be covered by fabric,” they explain. “The speaker insides consists of laser-cut wood to hold the tweeter and driver and encase the Raspberry Pi.”

The team aimed to design a speaker that would be at home in a kitchen. With a removable upper casing allowing for a choice of colour, the TILE can be customised to fit particular tastes and colour schemes.

Build your own speakers with Raspberry Pis

Raspberry Pi’s onboard audio jack, along with third-party HATs such as the HiFiBerry and Pimoroni Speaker pHAT, make speaker design and fabrication with the Pi an interesting alternative to pre-made tech. These builds don’t tend to be technically complex, and they provide some lovely examples of tech-based projects that reflect makers’ own particular aesthetic style.

If you have access to a 3D printer or a laser cutter, perhaps at a nearby maker space, then those can be excellent resources, but fancy kit isn’t a requirement. Basic joinery and crafting with card or paper are just a couple of ways you can build things that are all your own, using familiar tools and materials. We think more people would enjoy getting hands-on with this sort of thing if they gave it a whirl, and we publish a free magazine to help.

Raspberry Pi Zero AirPlay Speaker

Looking for a new project to build around the Raspberry Pi Zero, I came across the pHAT DAC from Pimoroni. This little add-on board adds audio playback capabilities to the Pi Zero. Because the pHAT uses the GPIO pins, the USB OTG port remains available for a wifi dongle.

This video by Frederick Vandenbosch is a great example of building AirPlay speakers using a Pi and HAT, and a quick search will find you lots more relevant tutorials and ideas.

Have you built your own? Share your speaker-based Pi builds with us in the comments.

The post 3D-printed speakers from the Technical University of Denmark appeared first on Raspberry Pi.

Continued: the answers to your questions for Eben Upton

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/eben-q-a-2/

Last week, we shared the first half of our Q&A with Raspberry Pi Trading CEO and Raspberry Pi creator Eben Upton. Today we follow up with all your other questions, including your expectations for a Raspberry Pi 4, Eben’s dream add-ons, and whether we really could go smaller than the Zero.

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

Get your questions to us now using #AskRaspberryPi on Twitter

With internet security becoming more necessary, will there be automated versions of VPN on an SD card?

There are already third-party tools which turn your Raspberry Pi into a VPN endpoint. Would we do it ourselves? Like the power button, it’s one of those cases where there are a million things we could do and so it’s more efficient to let the community get on with it.

Just to give a counterexample, while we don’t generally invest in optimising for particular use cases, we did invest a bunch of money into optimising Kodi to run well on Raspberry Pi, because we found that very large numbers of people were using it. So, if we find that we get half a million people a year using a Raspberry Pi as a VPN endpoint, then we’ll probably invest money into optimising it and feature it on the website as we’ve done with Kodi. But I don’t think we’re there today.

Have you ever seen any Pis running and doing important jobs in the wild, and if so, how does it feel?

It’s amazing how often you see them driving displays, for example in radio and TV studios. Of course, it feels great. There’s something wonderful about the geographic spread as well. The Raspberry Pi desktop is quite distinctive, both in its previous incarnation with the grey background and logo, and the current one where we have Greg Annandale’s road picture.

The PIXEL desktop on Raspberry Pi

And so it’s funny when you see it in places. Somebody sent me a video of them teaching in a classroom in rural Pakistan and in the background was Greg’s picture.

Raspberry Pi 4!?!

There will be a Raspberry Pi 4, obviously. We get asked about it a lot. I’m sticking to the guidance that I gave people that they shouldn’t expect to see a Raspberry Pi 4 this year. To some extent, the opportunity to do the 3B+ was a surprise: we were surprised that we’ve been able to get 200MHz more clock speed, triple the wireless and wired throughput, and better thermals, and still stick to the $35 price point.

We’re up against the wall from a silicon perspective; we’re at the end of what you can do with the 40nm process. It’s not that you couldn’t clock the processor faster, or put a larger processor which can execute more instructions per clock in there, it’s simply about the energy consumption and the fact that you can’t dissipate the heat. So we’ve got to go to a smaller process node and that’s an order of magnitude more challenging from an engineering perspective. There’s more effort, more risk, more cost, and all of those things are challenging.

With 3B+ out of the way, we’re going to start looking at this now. For the first six months or so we’re going to be figuring out exactly what people want from a Raspberry Pi 4. We’re listening to people’s comments about what they’d like to see in a new Raspberry Pi, and I’m hoping by early autumn we should have an idea of what we want to put in it and a strategy for how we might achieve that.

Could you go smaller than the Zero?

The challenge with Zero as that we’re periphery-limited. If you run your hand around the unit, there is no edge of that board that doesn’t have something there. So the question is: “If you want to go smaller than Zero, what feature are you willing to throw out?”

It’s a single-sided board, so you could certainly halve the PCB area if you fold the circuitry and use both sides, though you’d have to lose something. You could give up some GPIO and go back to 26 pins like the first Raspberry Pi. You could give up the camera connector, you could go to micro HDMI from mini HDMI. You could remove the SD card and just do USB boot. I’m inventing a product live on air! But really, you could get down to two thirds and lose a bunch of GPIO – it’s hard to imagine you could get to half the size.

What’s the one feature that you wish you could outfit on the Raspberry Pi that isn’t cost effective at this time? Your dream feature.

Well, more memory. There are obviously technical reasons why we don’t have more memory on there, but there are also market reasons. People ask “why doesn’t the Raspberry Pi have more memory?”, and my response is typically “go and Google ‘DRAM price’”. We’re used to the price of memory going down. And currently, we’re going through a phase where this has turned around and memory is getting more expensive again.

Machine learning would be interesting. There are machine learning accelerators which would be interesting to put on a piece of hardware. But again, they are not going to be used by everyone, so according to our method of pricing what we might add to a board, machine learning gets treated like a $50 chip. But that would be lovely to do.

Which citizen science projects using the Pi have most caught your attention?

I like the wildlife camera projects. We live out in the countryside in a little village, and we’re conscious of being surrounded by nature but we don’t see a lot of it on a day-to-day basis. So I like the nature cam projects, though, to my everlasting shame, I haven’t set one up yet. There’s a range of them, from very professional products to people taking a Raspberry Pi and a camera and putting them in a plastic box. So those are good fun.

Raspberry Shake seismometer

The Raspberry Shake seismometer

And there’s Meteor Pi from the Cambridge Science Centre, that’s a lot of fun. And the seismometer Raspberry Shake – that sort of thing is really nice. We missed the recent South Wales earthquake; perhaps we should set one up at our Californian office.

How does it feel to go to bed every day knowing you’ve changed the world for the better in such a massive way?

What feels really good is that when we started this in 2006 nobody else was talking about it, but now we’re part of a very broad movement.

We were in a really bad way: we’d seen a collapse in the number of applicants applying to study Computer Science at Cambridge and elsewhere. In our view, this reflected a move away from seeing technology as ‘a thing you do’ to seeing it as a ‘thing that you have done to you’. It is problematic from the point of view of the economy, industry, and academia, but most importantly it damages the life prospects of individual children, particularly those from disadvantaged backgrounds. The great thing about STEM subjects is that you can’t fake being good at them. There are a lot of industries where your Dad can get you a job based on who he knows and then you can kind of muddle along. But if your dad gets you a job building bridges and you suck at it, after the first or second bridge falls down, then you probably aren’t going to be building bridges anymore. So access to STEM education can be a great driver of social mobility.

By the time we were launching the Raspberry Pi in 2012, there was this wonderful movement going on. Code Club, for example, and CoderDojo came along. Lots of different ways of trying to solve the same problem. What feels really, really good is that we’ve been able to do this as part of an enormous community. And some parts of that community became part of the Raspberry Pi Foundation – we merged with Code Club, we merged with CoderDojo, and we continue to work alongside a lot of these other organisations. So in the two seconds it takes me to fall asleep after my face hits the pillow, that’s what I think about.

We’re currently advertising a Programme Manager role in New Delhi, India. Did you ever think that Raspberry Pi would be advertising a role like this when you were bringing together the Foundation?

No, I didn’t.

But if you told me we were going to be hiring somewhere, India probably would have been top of my list because there’s a massive IT industry in India. When we think about our interaction with emerging markets, India, in a lot of ways, is the poster child for how we would like it to work. There have already been some wonderful deployments of Raspberry Pi, for example in Kerala, without our direct involvement. And we think we’ve got something that’s useful for the Indian market. We have a product, we have clubs, we have teacher training. And we have a body of experience in how to teach people, so we have a physical commercial product as well as a charitable offering that we think are a good fit.

It’s going to be massive.

What is your favourite BBC type-in listing?

There was a game called Codename: Druid. There is a famous game called Codename: Droid which was the sequel to Stryker’s Run, which was an awesome, awesome game. And there was a type-in game called Codename: Druid, which was at the bottom end of what you would consider a commercial game.

codename druid

And I remember typing that in. And what was really cool about it was that the next month, the guy who wrote it did another article that talks about the memory map and which operating system functions used which bits of memory. So if you weren’t going to do disc access, which bits of memory could you trample on and know the operating system would survive.

babbage versus bugs Raspberry Pi annual

See the full listing for Babbage versus Bugs in the Raspberry Pi 2018 Annual

I still like type-in listings. The Raspberry Pi 2018 Annual has a type-in listing that I wrote for a Babbage versus Bugs game. I will say that’s not the last type-in listing you will see from me in the next twelve months. And if you download the PDF, you could probably copy and paste it into your favourite text editor to save yourself some time.

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

Tips for Success: GDPR Lessons Learned

Post Syndicated from Chad Woolf original https://aws.amazon.com/blogs/security/tips-for-success-gdpr-lessons-learned/

Security is our top priority at AWS, and from the beginning we have built security into the fabric of our services. With the introduction of GDPR (which becomes enforceable on May 25 of 2018), privacy and data protection have become even more ingrained into our security-centered culture. Three weeks ago, well ahead of the deadline, we announced that all AWS services are compliant with GDPR, meaning you can use AWS as a data processor as a way to help solve your GDPR challenges (be sure to visit our GDPR Center for additional information).

When it comes to GDPR compliance, many customers are progressing nicely and much of the initial trepidation is gone. In my interactions with customers on this topic, a few themes have emerged as universal:

  • GDPR is important. You need to have a plan in place if you process personal data of EU data subjects, not only because it’s good governance, but because GDPR does carry significant penalties for non-compliance.
  • Solving this can be complex, potentially involving a lot of personnel and multiple tools. Your GDPR process will also likely span across disciplines – impacting people, processes, and technology.
  • Each customer is unique, and there are many methodologies around assessing your compliance with GDPR. It’s important to be aware of your own individual business attributes.

I thought it might be helpful to share some of our own lessons learned. In our experience in solving the GDPR challenge, the following were keys to our success:

  1. Get your senior leadership involved. We have a regular cadence of detailed status conversations about GDPR with our CEO, Andy Jassy. GDPR is high stakes, and the AWS leadership team knows it. If GDPR doesn’t have the attention it needs with the visibility of top management today, it’s time to escalate.
  2. Centralize the GDPR efforts. Driving all work streams centrally is key. This may sound obvious, but managing this in a distributed manner may result in duplicative effort and/or team members moving in a different direction.
  3. The most important single partner in solving GDPR is your legal team. Having non-legal people make assumptions about how to interpret GDPR for your unique environment is both risky and a potential waste of time and resources. You want to avoid analysis paralysis by getting proper legal advice, collaborating on a direction, and then moving forward with the proper urgency.
  4. Collaborate closely with tech leadership. The “process” people in your organization, the ones who already know how to approach governance problems, are typically comfortable jumping right in to GDPR. But technical teams, including data owners, have set up their software for business application. They may not even know what kind of data they are storing, processing, or transferring to other parts of the business. In the GDPR exercise they need to be aware of (or at least help facilitate) the tracking of data and data elements between systems. This isn’t a typical ask for technical teams, so be prepared to educate and to fully understand data flow.
  5. Don’t live by the established checklists. There are multiple methodologies to solving the compliance challenges of GDPR. At AWS, we ended up establishing core requirements, mapped out by data controller and data processor functions and then, in partnership with legal, decided upon a group of projects based on our known current state. Be careful about using a set methodology, tool or questionnaire to govern your efforts. These generic assessments can help educate, but letting them drive or limit your work could lead to missing something that is key to your own compliance. In this sense, a generic, “one size fits all” solution might not be helpful.
  6. Don’t be afraid to challenge prior orthodoxy. Many times we changed course based on new information. You shouldn’t be afraid to scrap an effort if you determine it’s not working. You should also not be afraid to escalate issues to senior leadership when needed. This is an executive issue.
  7. Look for ways to leverage your work beyond this compliance activity. GDPR requires serious effort, but are the results limited to GDPR compliance? Certainly not. You can use GDPR workflows as a way to ensure better governance moving forward. Privacy and security will require work for the foreseeable future, so make your governance program scalable and usable for other purposes.

One last tip that has made all the difference: think about protecting data subjects and work backwards from there. Customer focus drives us to ask, “what would customers and data subjects want and expect us to do?” Taking GDPR from a pure legal or compliance standpoint may be technically sufficient, but we believe the objectives of security and personal data protection require a more comprehensive view, and you can most effectively shape that view by starting with the individuals GDPR was meant to protect.

If you would like to find out more about our experiences, as well as how we can help you in your efforts, please reach out to us today.

-Chad Woolf

Vice President, AWS Security Assurance

Interested in additional AWS Security news? Follow the AWS Security Blog on Twitter.

[$] A successful defense against a copyright troll

Post Syndicated from jake original https://lwn.net/Articles/752485/rss

At the 2018 Legal and
Licensing Workshop
(LLW), which is a yearly gathering
of lawyers and technical folks organized by the Free Software Foundation
Europe (FSFE), attendees got more details on a recent hearing in a German GPL
enforcement case. Marcus von Welser is a lawyer who represented the
defendant, Geniatech,
in a case that was brought by Patrick
McHardy
. In the presentation, von
Welser was joined by
Armijn Hemel, who helped
Geniatech in its compliance efforts. The hearing
was of interest for a number of reasons, not least because McHardy
withdrew his request for an injunction once it became clear that the judge
was leaning in
favor of the defendants
—effectively stopping this case dead in its tracks.

OMG The Stupid It Burns

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/04/omg-stupid-it-burns.html

This article, pointed out by @TheGrugq, is stupid enough that it’s worth rebutting.

The article starts with the question “Why did the lessons of Stuxnet, Wannacry, Heartbleed and Shamoon go unheeded?“. It then proceeds to ignore the lessons of those things.
Some of the actual lessons should be things like how Stuxnet crossed air gaps, how Wannacry spread through flat Windows networking, how Heartbleed comes from technical debt, and how Shamoon furthers state aims by causing damage.
But this article doesn’t cover the technical lessons. Instead, it thinks the lesson should be the moral lesson, that we should take these things more seriously. But that’s stupid. It’s the sort of lesson people teach you that know nothing about the topic. When you have nothing of value to contribute to a topic you can always take the moral high road and criticize everyone for being morally weak for not taking it more seriously. Obviously, since doctors haven’t cured cancer yet, it’s because they don’t take the problem seriously.
The article continues to ignore the lesson of these cyber attacks and instead regales us with a list of military lessons from WW I and WW II. This makes the same flaw that many in the military make, trying to understand cyber through analogies with the real world. It’s not that such lessons could have no value, it’s that this article contains a poor list of them. It seems to consist of a random list of events that appeal to the author rather than events that have bearing on cybersecurity.
Then, in case we don’t get the point, the article bullies us with hyperbole, cliches, buzzwords, bombastic language, famous quotes, and citations. It’s hard to see how most of them actually apply to the text. Rather, it seems like they are included simply because he really really likes them.
The article invests much effort in discussing the buzzword “OODA loop”. Most attacks in cyberspace don’t have one. Instead, attackers flail around, trying lots of random things, overcoming defense with brute-force rather than an understanding of what’s going on. That’s obviously the case with Wannacry: it was an accident, with the perpetrator experimenting with what would happen if they added the ETERNALBLUE exploit to their existing ransomware code. The consequence was beyond anybody’s ability to predict.
You might claim that this is just the first stage, that they’ll loop around, observe Wannacry’s effects, orient themselves, decide, then act upon what they learned. Nope. Wannacry burned the exploit. It’s essentially removed any vulnerable systems from the public Internet, thereby making it impossible to use what they learned. It’s still active a year later, with infected systems behind firewalls busily scanning the Internet so that if you put a new system online that’s vulnerable, it’ll be taken offline within a few hours, before any other evildoer can take advantage of it.
See what I’m doing here? Learning the actual lessons of things like Wannacry? The thing the above article fails to do??
The article has a humorous paragraph on “defense in depth”, misunderstanding the term. To be fair, it’s the cybersecurity industry’s fault: they adopted then redefined the term. That’s why there’s two separate articles on Wikipedia: one for the old military term (as used in this article) and one for the new cybersecurity term.
As used in the cybersecurity industry, “defense in depth” means having multiple layers of security. Many organizations put all their defensive efforts on the perimeter, and none inside a network. The idea of “defense in depth” is to put more defenses inside the network. For example, instead of just one firewall at the edge of the network, put firewalls inside the network to segment different subnetworks from each other, so that a ransomware infection in the customer support computers doesn’t spread to sales and marketing computers.
The article talks about exploiting WiFi chips to bypass the defense in depth measures like browser sandboxes. This is conflating different types of attacks. A WiFi attack is usually considered a local attack, from somebody next to you in bar, rather than a remote attack from a server in Russia. Moreover, far from disproving “defense in depth” such WiFi attacks highlight the need for it. Namely, phones need to be designed so that successful exploitation of other microprocessors (namely, the WiFi, Bluetooth, and cellular baseband chips) can’t directly compromise the host system. In other words, once exploited with “Broadpwn”, a hacker would need to extend the exploit chain with another vulnerability in the hosts Broadcom WiFi driver rather than immediately exploiting a DMA attack across PCIe. This suggests that if PCIe is used to interface to peripherals in the phone that an IOMMU be used, for “defense in depth”.
Cybersecurity is a young field. There are lots of useful things that outsider non-techies can teach us. Lessons from military history would be well-received.
But that’s not this story. Instead, this story is by an outsider telling us we don’t know what we are doing, that they do, and then proceeds to prove they don’t know what they are doing. Their argument is based on a moral suasion and bullying us with what appears on the surface to be intellectual rigor, but which is in fact devoid of anything smart.
My fear, here, is that I’m going to be in a meeting where somebody has read this pretentious garbage, explaining to me why “defense in depth” is wrong and how we need to OODA faster. I’d rather nip this in the bud, pointing out if you found anything interesting from that article, you are wrong.

Implement continuous integration and delivery of serverless AWS Glue ETL applications using AWS Developer Tools

Post Syndicated from Prasad Alle original https://aws.amazon.com/blogs/big-data/implement-continuous-integration-and-delivery-of-serverless-aws-glue-etl-applications-using-aws-developer-tools/

AWS Glue is an increasingly popular way to develop serverless ETL (extract, transform, and load) applications for big data and data lake workloads. Organizations that transform their ETL applications to cloud-based, serverless ETL architectures need a seamless, end-to-end continuous integration and continuous delivery (CI/CD) pipeline: from source code, to build, to deployment, to product delivery. Having a good CI/CD pipeline can help your organization discover bugs before they reach production and deliver updates more frequently. It can also help developers write quality code and automate the ETL job release management process, mitigate risk, and more.

AWS Glue is a fully managed data catalog and ETL service. It simplifies and automates the difficult and time-consuming tasks of data discovery, conversion, and job scheduling. AWS Glue crawls your data sources and constructs a data catalog using pre-built classifiers for popular data formats and data types, including CSV, Apache Parquet, JSON, and more.

When you are developing ETL applications using AWS Glue, you might come across some of the following CI/CD challenges:

  • Iterative development with unit tests
  • Continuous integration and build
  • Pushing the ETL pipeline to a test environment
  • Pushing the ETL pipeline to a production environment
  • Testing ETL applications using real data (live test)
  • Exploring and validating data

In this post, I walk you through a solution that implements a CI/CD pipeline for serverless AWS Glue ETL applications supported by AWS Developer Tools (including AWS CodePipeline, AWS CodeCommit, and AWS CodeBuild) and AWS CloudFormation.

Solution overview

The following diagram shows the pipeline workflow:

This solution uses AWS CodePipeline, which lets you orchestrate and automate the test and deploy stages for ETL application source code. The solution consists of a pipeline that contains the following stages:

1.) Source Control: In this stage, the AWS Glue ETL job source code and the AWS CloudFormation template file for deploying the ETL jobs are both committed to version control. I chose to use AWS CodeCommit for version control.

To get the ETL job source code and AWS CloudFormation template, download the gluedemoetl.zip file. This solution is developed based on a previous post, Build a Data Lake Foundation with AWS Glue and Amazon S3.

2.) LiveTest: In this stage, all resources—including AWS Glue crawlers, jobs, S3 buckets, roles, and other resources that are required for the solution—are provisioned, deployed, live tested, and cleaned up.

The LiveTest stage includes the following actions:

  • Deploy: In this action, all the resources that are required for this solution (crawlers, jobs, buckets, roles, and so on) are provisioned and deployed using an AWS CloudFormation template.
  • AutomatedLiveTest: In this action, all the AWS Glue crawlers and jobs are executed and data exploration and validation tests are performed. These validation tests include, but are not limited to, record counts in both raw tables and transformed tables in the data lake and any other business validations. I used AWS CodeBuild for this action.
  • LiveTestApproval: This action is included for the cases in which a pipeline administrator approval is required to deploy/promote the ETL applications to the next stage. The pipeline pauses in this action until an administrator manually approves the release.
  • LiveTestCleanup: In this action, all the LiveTest stage resources, including test crawlers, jobs, roles, and so on, are deleted using the AWS CloudFormation template. This action helps minimize cost by ensuring that the test resources exist only for the duration of the AutomatedLiveTest and LiveTestApproval

3.) DeployToProduction: In this stage, all the resources are deployed using the AWS CloudFormation template to the production environment.

Try it out

This code pipeline takes approximately 20 minutes to complete the LiveTest test stage (up to the LiveTest approval stage, in which manual approval is required).

To get started with this solution, choose Launch Stack:

This creates the CI/CD pipeline with all of its stages, as described earlier. It performs an initial commit of the sample AWS Glue ETL job source code to trigger the first release change.

In the AWS CloudFormation console, choose Create. After the template finishes creating resources, you see the pipeline name on the stack Outputs tab.

After that, open the CodePipeline console and select the newly created pipeline. Initially, your pipeline’s CodeCommit stage shows that the source action failed.

Allow a few minutes for your new pipeline to detect the initial commit applied by the CloudFormation stack creation. As soon as the commit is detected, your pipeline starts. You will see the successful stage completion status as soon as the CodeCommit source stage runs.

In the CodeCommit console, choose Code in the navigation pane to view the solution files.

Next, you can watch how the pipeline goes through the LiveTest stage of the deploy and AutomatedLiveTest actions, until it finally reaches the LiveTestApproval action.

At this point, if you check the AWS CloudFormation console, you can see that a new template has been deployed as part of the LiveTest deploy action.

At this point, make sure that the AWS Glue crawlers and the AWS Glue job ran successfully. Also check whether the corresponding databases and external tables have been created in the AWS Glue Data Catalog. Then verify that the data is validated using Amazon Athena, as shown following.

Open the AWS Glue console, and choose Databases in the navigation pane. You will see the following databases in the Data Catalog:

Open the Amazon Athena console, and run the following queries. Verify that the record counts are matching.

SELECT count(*) FROM "nycitytaxi_gluedemocicdtest"."data";
SELECT count(*) FROM "nytaxiparquet_gluedemocicdtest"."datalake";

The following shows the raw data:

The following shows the transformed data:

The pipeline pauses the action until the release is approved. After validating the data, manually approve the revision on the LiveTestApproval action on the CodePipeline console.

Add comments as needed, and choose Approve.

The LiveTestApproval stage now appears as Approved on the console.

After the revision is approved, the pipeline proceeds to use the AWS CloudFormation template to destroy the resources that were deployed in the LiveTest deploy action. This helps reduce cost and ensures a clean test environment on every deployment.

Production deployment is the final stage. In this stage, all the resources—AWS Glue crawlers, AWS Glue jobs, Amazon S3 buckets, roles, and so on—are provisioned and deployed to the production environment using the AWS CloudFormation template.

After successfully running the whole pipeline, feel free to experiment with it by changing the source code stored on AWS CodeCommit. For example, if you modify the AWS Glue ETL job to generate an error, it should make the AutomatedLiveTest action fail. Or if you change the AWS CloudFormation template to make its creation fail, it should affect the LiveTest deploy action. The objective of the pipeline is to guarantee that all changes that are deployed to production are guaranteed to work as expected.

Conclusion

In this post, you learned how easy it is to implement CI/CD for serverless AWS Glue ETL solutions with AWS developer tools like AWS CodePipeline and AWS CodeBuild at scale. Implementing such solutions can help you accelerate ETL development and testing at your organization.

If you have questions or suggestions, please comment below.

 


Additional Reading

If you found this post useful, be sure to check out Implement Continuous Integration and Delivery of Apache Spark Applications using AWS and Build a Data Lake Foundation with AWS Glue and Amazon S3.

 


About the Authors

Prasad Alle is a Senior Big Data Consultant with AWS Professional Services. He spends his time leading and building scalable, reliable Big data, Machine learning, Artificial Intelligence and IoT solutions for AWS Enterprise and Strategic customers. His interests extend to various technologies such as Advanced Edge Computing, Machine learning at Edge. In his spare time, he enjoys spending time with his family.

 
Luis Caro is a Big Data Consultant for AWS Professional Services. He works with our customers to provide guidance and technical assistance on big data projects, helping them improving the value of their solutions when using AWS.

 

 

 

Audit Trail Overview

Post Syndicated from Bozho original https://techblog.bozho.net/audit-trail-overview/

As part of my current project (secure audit trail) I decided to make a survey about the use of audit trail “in the wild”.

I haven’t written in details about this project of mine (unlike with some other projects). Mostly because it’s commercial and I don’t want to use my blog as a direct promotion channel (though I am doing that at the moment, ironically). But the aim of this post is to shed some light on how audit trail is used.

The survey can be found here. The questions are basically: does your current project have audit trail functionality, and if yes, is it protected from tampering. If not – do you think you should have such functionality.

The results are interesting (although with only around 50 respondents)

So more than half of the systems (on which respondents are working) don’t have audit trail. While audit trail is recommended by information security and related standards, it may not find place in the “busy schedule” of a software project, even though it’s fairly easy to provide a trivial implementation (e.g. I’ve written how to quickly setup one with Hibernate and Spring)

A trivial implementation might do in many cases but if the audit log is critical (e.g. access to sensitive data, performing financial operations etc.), then relying on a trivial implementation might not be enough. In other words – if the sysadmin can access the database and delete or modify the audit trail, then it doesn’t serve much purpose. Hence the next question – how is the audit trail protected from tampering:

And apparently, from the less than 50% of projects with audit trail, around 50% don’t have technical guarantees that the audit trail can’t be tampered with. My guess is it’s more, because people have different understanding of what technical measures are sufficient. E.g. someone may think that digitally signing your log files (or log records) is sufficient, but in fact it isn’t, as whole files (or records) can be deleted (or fully replaced) without a way to detect that. Timestamping can help (and a good audit trail solution should have that), but it doesn’t guarantee the order of events or prevent a malicious actor from deleting or inserting fake ones. And if timestamping is done on a log file level, then any not-yet-timestamped log file is vulnerable to manipulation.

I’ve written about event logs before and their two flavours – event sourcing and audit trail. An event log can effectively be considered audit trail, but you’d need additional security to avoid the problems mentioned above.

So, let’s see what would various levels of security and usefulness of audit logs look like. There are many papers on the topic (e.g. this and this), and they often go into the intricate details of how logging should be implemented. I’ll try to give an overview of the approaches:

  • Regular logs – rely on regular INFO log statements in the production logs to look for hints of what has happened. This may be okay, but is harder to look for evidence (as there is non-auditable data in those log files as well), and it’s not very secure – usually logs are collected (e.g. with graylog) and whoever has access to the log collector’s database (or search engine in the case of Graylog), can manipulate the data and not be caught
  • Designated audit trail – whether it’s stored in the database or in logs files. It has the proper business-event level granularity, but again doesn’t prevent or detect tampering. With lower risk systems that may is perfectly okay.
  • Timestamped logs – whether it’s log files or (harder to implement) database records. Timestamping is good, but if it’s not an external service, a malicious actor can get access to the local timestamping service and issue fake timestamps to either re-timestamp tampered files. Even if the timestamping is not compromised, whole entries can be deleted. The fact that they are missing can sometimes be deduced based on other factors (e.g. hour of rotation), but regularly verifying that is extra effort and may not always be feasible.
  • Hash chaining – each entry (or sequence of log files) could be chained (just as blockchain transactions) – the next one having the hash of the previous one. This is a good solution (whether it’s local, external or 3rd party), but it has the risk of someone modifying or deleting a record, getting your entire chain and re-hashing it. All the checks will pass, but the data will not be correct
  • Hash chaining with anchoring – the head of the chain (the hash of the last entry/block) could be “anchored” to an external service that is outside the capabilities of a malicious actor. Ideally, a public blockchain, alternatively – paper, a public service (twitter), email, etc. That way a malicious actor can’t just rehash the whole chain, because any check against the external service would fail.
  • WORM storage (write once, ready many). You could send your audit logs almost directly to WORM storage, where it’s impossible to replace data. However, that is not ideal, as WORM storage can be slow and expensive. For example AWS Glacier has rather big retrieval times and searching through recent data makes it impractical. It’s actually cheaper than S3, for example, and you can have expiration policies. But having to support your own WORM storage is expensive. It is a good idea to eventually send the logs to WORM storage, but “fresh” audit trail should probably not be “archived” so that it’s searchable and some actionable insight can be gained from it.
  • All-in-one – applying all of the above “just in case” may be unnecessary for every project out there, but that’s what I decided to do at LogSentinel. Business-event granularity with timestamping, hash chaining, anchoring, and eventually putting to WORM storage – I think that provides both security guarantees and flexibility.

I hope the overview is useful and the results from the survey shed some light on how this aspect of information security is underestimated.

The post Audit Trail Overview appeared first on Bozho's tech blog.

Achieving Major Stability and Performance Improvements in Yahoo Mail with a Novel Redux Architecture

Post Syndicated from mikesefanov original https://yahooeng.tumblr.com/post/173062946866

yahoodevelopers:

By Mohit Goenka, Gnanavel Shanmugam, and Lance Welsh

At Yahoo Mail, we’re constantly striving to upgrade our product experience. We do this not only by adding new features based on our members’ feedback, but also by providing the best technical solutions to power the most engaging experiences. As such, we’ve recently introduced a number of novel and unique revisions to the way in which we use Redux that have resulted in significant stability and performance improvements. Developers may find our methods useful in achieving similar results in their apps.

Improvements to product metrics

Last year Yahoo Mail implemented a brand new architecture using Redux. Since then, we have transformed the overall architecture to reduce latencies in various operations, reduce JavaScript exceptions, and better synchronized states. As a result, the product is much faster and more stable.

Stability improvements:

  • when checking for new emails – 20%
  • when reading emails – 30%
  • when sending emails – 20%

Performance improvements:

  • 10% improvement in page load performance
  • 40% improvement in frame rendering time

We have also reduced API calls by approximately 20%.

How we use Redux in Yahoo Mail

Redux architecture is reliant on one large store that represents the application state. In a Redux cycle, action creators dispatch actions to change the state of the store. React Components then respond to those state changes. We’ve made some modifications on top of this architecture that are atypical in the React-Redux community.

For instance, when fetching data over the network, the traditional methodology is to use Thunk middleware. Yahoo Mail fetches data over the network from our API. Thunks would create an unnecessary and undesirable dependency between the action creators and our API. If and when the API changes, the action creators must then also change. To keep these concerns separate we dispatch the action payload from the action creator to store them in the Redux state for later processing by “action syncers”. Action syncers use the payload information from the store to make requests to the API and process responses. In other words, the action syncers form an API layer by interacting with the store. An additional benefit to keeping the concerns separate is that the API layer can change as the backend changes, thereby preventing such changes from bubbling back up into the action creators and components. This also allowed us to optimize the API calls by batching, deduping, and processing the requests only when the network is available. We applied similar strategies for handling other side effects like route handling and instrumentation. Overall, action syncers helped us to reduce our API calls by ~20% and bring down API errors by 20-30%.

Another change to the normal Redux architecture was made to avoid unnecessary props. The React-Redux community has learned to avoid passing unnecessary props from high-level components through multiple layers down to lower-level components (prop drilling) for rendering. We have introduced action enhancers middleware to avoid passing additional unnecessary props that are purely used when dispatching actions. Action enhancers add data to the action payload so that data does not have to come from the component when dispatching the action. This avoids the component from having to receive that data through props and has improved frame rendering by ~40%. The use of action enhancers also avoids writing utility functions to add commonly-used data to each action from action creators.

image

In our new architecture, the store reducers accept the dispatched action via action enhancers to update the state. The store then updates the UI, completing the action cycle. Action syncers then initiate the call to the backend APIs to synchronize local changes.

Conclusion

Our novel use of Redux in Yahoo Mail has led to significant user-facing benefits through a more performant application. It has also reduced development cycles for new features due to its simplified architecture. We’re excited to share our work with the community and would love to hear from anyone interested in learning more.

The DMCA and its Chilling Effects on Research

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

The Center for Democracy and Technology has a good summary of the current state of the DMCA’s chilling effects on security research.

To underline the nature of chilling effects on hacking and security research, CDT has worked to describe how tinkerers, hackers, and security researchers of all types both contribute to a baseline level of security in our digital environment and, in turn, are shaped themselves by this environment, most notably when things they do upset others and result in threats, potential lawsuits, and prosecution. We’ve published two reports (sponsored by the Hewlett Foundation and MacArthur Foundation) about needed reforms to the law and the myriad of ways that security research directly improves people’s lives. To get a more complete picture, we wanted to talk to security researchers themselves and gauge the forces that shape their work; essentially, we wanted to “take the pulse” of the security research community.

Today, we are releasing a third report in service of this effort: “Taking the Pulse of Hacking: A Risk Basis for Security Research.” We report findings after having interviewed a set of 20 security researchers and hackers — half academic and half non-academic — about what considerations they take into account when starting new projects or engaging in new work, as well as to what extent they or their colleagues have faced threats in the past that chilled their work. The results in our report show that a wide variety of constraints shape the work they do, from technical constraints to ethical boundaries to legal concerns, including the DMCA and especially the CFAA.

Note: I am a signatory on the letter supporting unrestricted security research.