Tag Archives: Plex

[$] Deferring seccomp decisions to user space

Post Syndicated from corbet original https://lwn.net/Articles/756233/rss

There has been a lot of work in recent years to use BPF to push policy
decisions into the kernel. But sometimes, it seems, what is really wanted
is a way for a BPF program to punt a decision back to user space. That is
the objective behind this patch set giving
the secure
computing (seccomp)
mechanism a way to pass complex decisions to
a user-space helper program.

Friday Squid Blogging: Do Cephalopods Contain Alien DNA?

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/06/friday_squid_bl_627.html

Maybe not DNA, but biological somethings.

Cause of Cambrian explosion — Terrestrial or Cosmic?“:

Abstract: We review the salient evidence consistent with or predicted by the Hoyle-Wickramasinghe (H-W) thesis of Cometary (Cosmic) Biology. Much of this physical and biological evidence is multifactorial. One particular focus are the recent studies which date the emergence of the complex retroviruses of vertebrate lines at or just before the Cambrian Explosion of ~500 Ma. Such viruses are known to be plausibly associated with major evolutionary genomic processes. We believe this coincidence is not fortuitous but is consistent with a key prediction of H-W theory whereby major extinction-diversification evolutionary boundaries coincide with virus-bearing cometary-bolide bombardment events. A second focus is the remarkable evolution of intelligent complexity (Cephalopods) culminating in the emergence of the Octopus. A third focus concerns the micro-organism fossil evidence contained within meteorites as well as the detection in the upper atmosphere of apparent incoming life-bearing particles from space. In our view the totality of the multifactorial data and critical analyses assembled by Fred Hoyle, Chandra Wickramasinghe and their many colleagues since the 1960s leads to a very plausible conclusion — life may have been seeded here on Earth by life-bearing comets as soon as conditions on Earth allowed it to flourish (about or just before 4.1 Billion years ago); and living organisms such as space-resistant and space-hardy bacteria, viruses, more complex eukaryotic cells, fertilised ova and seeds have been continuously delivered ever since to Earth so being one important driver of further terrestrial evolution which has resulted in considerable genetic diversity and which has led to the emergence of mankind.

Two commentaries.

This is almost certainly not true.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

Majority of Canadians Consume Online Content Legally, Survey Finds

Post Syndicated from Andy original https://torrentfreak.com/majority-of-canadians-consume-online-content-legally-survey-finds-180531/

Back in January, a coalition of companies and organizations with ties to the entertainment industries called on local telecoms regulator CRTC to implement a national website blocking regime.

Under the banner of Fairplay Canada, members including Bell, Cineplex, Directors Guild of Canada, Maple Leaf Sports and Entertainment, Movie Theatre Association of Canada, and Rogers Media, spoke of an industry under threat from marauding pirates. But just how serious is this threat?

The results of a new survey commissioned by Innovation Science and Economic Development Canada (ISED) in collaboration with the Department of Canadian Heritage (PCH) aims to shine light on the problem by revealing the online content consumption habits of citizens in the Great White North.

While there are interesting findings for those on both sides of the site-blocking debate, the situation seems somewhat removed from the Armageddon scenario predicted by the entertainment industries.

Carried out among 3,301 Canadians aged 12 years and over, the Kantar TNS study aims to cover copyright infringement in six key content areas – music, movies, TV shows, video games, computer software, and eBooks. Attitudes and behaviors are also touched upon while measuring the effectiveness of Canada’s copyright measures.

General Digital Content Consumption

In its introduction, the report notes that 28 million Canadians used the Internet in the three-month study period to November 27, 2017. Of those, 22 million (80%) consumed digital content. Around 20 million (73%) streamed or accessed content, 16 million (59%) downloaded content, while 8 million (28%) shared content.

Music, TV shows and movies all battled for first place in the consumption ranks, with 48%, 48%, and 46% respectively.

Copyright Infringement

According to the study, the majority of Canadians do things completely by the book. An impressive 74% of media-consuming respondents said that they’d only accessed material from legal sources in the preceding three months.

The remaining 26% admitted to accessing at least one illegal file in the same period. Of those, just 5% said that all of their consumption was from illegal sources, with movies (36%), software (36%), TV shows (34%) and video games (33%) the most likely content to be consumed illegally.

Interestingly, the study found that few demographic factors – such as gender, region, rural and urban, income, employment status and language – play a role in illegal content consumption.

“We found that only age and income varied significantly between consumers who infringed by downloading or streaming/accessing content online illegally and consumers who did not consume infringing content online,” the report reads.

“More specifically, the profile of consumers who downloaded or streamed/accessed infringing content skewed slightly younger and towards individuals with household incomes of $100K+.”

Licensed services much more popular than pirate haunts

It will come as no surprise that Netflix was the most popular service with consumers, with 64% having used it in the past three months. Sites like YouTube and Facebook were a big hit too, visited by 36% and 28% of content consumers respectively.

Overall, 74% of online content consumers use licensed services for content while 42% use social networks. Under a third (31%) use a combination of peer-to-peer (BitTorrent), cyberlocker platforms, or linking sites. Stream-ripping services are used by 9% of content consumers.

“Consumers who reported downloading or streaming/accessing infringing content only are less likely to use licensed services and more likely to use peer-to-peer/cyberlocker/linking sites than other consumers of online content,” the report notes.

Attitudes towards legal consumption & infringing content

In common with similar surveys over the years, the Kantar research looked at the reasons why people consume content from various sources, both legal and otherwise.

Convenience (48%), speed (36%) and quality (34%) were the most-cited reasons for using legal sources. An interesting 33% of respondents said they use legal sites to avoid using illegal sources.

On the illicit front, 54% of those who obtained unauthorized content in the previous three months said they did so due to it being free, with 40% citing convenience and 34% mentioning speed.

Almost six out of ten (58%) said lower costs would encourage them to switch to official sources, with 47% saying they’d move if legal availability was improved.

Canada’s ‘Notice-and-Notice’ warning system

People in Canada who share content on peer-to-peer systems like BitTorrent without permission run the risk of receiving an infringement notice warning them to stop. These are sent by copyright holders via users’ ISPs and the hope is that the shock of receiving a warning will turn consumers back to the straight and narrow.

The study reveals that 10% of online content consumers over the age of 12 have received one of these notices but what kind of effect have they had?

“Respondents reported that receiving such a notice resulted in the following: increased awareness of copyright infringement (38%), taking steps to ensure password protected home networks (27%), a household discussion about copyright infringement (27%), and discontinuing illegal downloading or streaming (24%),” the report notes.

While these are all positives for the entertainment industries, Kantar reports that almost a quarter (24%) of people who receive a notice simply ignore them.

Stream-ripping

Once upon a time, people obtaining music via P2P networks was cited as the music industry’s greatest threat but, with the advent of sites like YouTube, so-called stream-ripping is the latest bogeyman.

According to the study, 11% of Internet users say they’ve used a stream-ripping service. They are most likely to be male (62%) and predominantly 18 to 34 (52%) years of age.

“Among Canadians who have used a service to stream-rip music or entertainment, nearly half (48%) have used stream-ripping sites, one-third have used downloader apps (38%), one-in-seven (14%) have used a stream-ripping plug-in, and one-in-ten (10%) have used stream-ripping software,” the report adds.

Set-Top Boxes and VPNs

Few general piracy studies would be complete in 2018 without touching on set-top devices and Virtual Private Networks and this report doesn’t disappoint.

More than one in five (21%) respondents aged 12+ reported using a VPN, with the main purpose of securing communications and Internet browsing (57%).

A relatively modest 36% said they use a VPN to access free content while 32% said the aim was to access geo-blocked content unavailable in Canada. Just over a quarter (27%) said that accessing content from overseas at a reasonable price was the main motivator.

One in ten (10%) of respondents reported using a set-top box, with 78% stating they use them to access paid-for content. Interestingly, only a small number say they use the devices to infringe.

“A minority use set-top boxes to access other content that is not legal or they are unsure if it is legal (16%), or to access live sports that are not legal or they are unsure if it is legal (11%),” the report notes.

“Individuals who consumed a mix of legal and illegal content online are more likely to use VPN services (42%) or TV set-top boxes (21%) than consumers who only downloaded or streamed/accessed legal content.”

Kantar says that the findings of the report will be used to help policymakers evaluate how Canada’s Copyright Act is coping with a changing market and technological developments.

“This research will provide the necessary information required to further develop copyright policy in Canada, as well as to provide a foundation to assess the effectiveness of the measures to address copyright infringement, should future analysis be undertaken,” it concludes.

The full report can be found here (pdf)

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

Use Slack ChatOps to Deploy Your Code – How to Integrate Your Pipeline in AWS CodePipeline with Your Slack Channel

Post Syndicated from Rumi Olsen original https://aws.amazon.com/blogs/devops/use-slack-chatops-to-deploy-your-code-how-to-integrate-your-pipeline-in-aws-codepipeline-with-your-slack-channel/

Slack is widely used by DevOps and development teams to communicate status. Typically, when a build has been tested and is ready to be promoted to a staging environment, a QA engineer or DevOps engineer kicks off the deployment. Using Slack in a ChatOps collaboration model, the promotion can be done in a single click from a Slack channel. And because the promotion happens through a Slack channel, the whole development team knows what’s happening without checking email.

In this blog post, I will show you how to integrate AWS services with a Slack application. I use an interactive message button and incoming webhook to promote a stage with a single click.

To follow along with the steps in this post, you’ll need a pipeline in AWS CodePipeline. If you don’t have a pipeline, the fastest way to create one for this use case is to use AWS CodeStar. Go to the AWS CodeStar console and select the Static Website template (shown in the screenshot). AWS CodeStar will create a pipeline with an AWS CodeCommit repository and an AWS CodeDeploy deployment for you. After the pipeline is created, you will need to add a manual approval stage.

You’ll also need to build a Slack app with webhooks and interactive components, write two Lambda functions, and create an API Gateway API and a SNS topic.

As you’ll see in the following diagram, when I make a change and merge a new feature into the master branch in AWS CodeCommit, the check-in kicks off my CI/CD pipeline in AWS CodePipeline. When CodePipeline reaches the approval stage, it sends a notification to Amazon SNS, which triggers an AWS Lambda function (ApprovalRequester).

The Slack channel receives a prompt that looks like the following screenshot. When I click Yes to approve the build promotion, the approval result is sent to CodePipeline through API Gateway and Lambda (ApprovalHandler). The pipeline continues on to deploy the build to the next environment.

Create a Slack app

For App Name, type a name for your app. For Development Slack Workspace, choose the name of your workspace. You’ll see in the following screenshot that my workspace is AWS ChatOps.

After the Slack application has been created, you will see the Basic Information page, where you can create incoming webhooks and enable interactive components.

To add incoming webhooks:

  1. Under Add features and functionality, choose Incoming Webhooks. Turn the feature on by selecting Off, as shown in the following screenshot.
  2. Now that the feature is turned on, choose Add New Webhook to Workspace. In the process of creating the webhook, Slack lets you choose the channel where messages will be posted.
  3. After the webhook has been created, you’ll see its URL. You will use this URL when you create the Lambda function.

If you followed the steps in the post, the pipeline should look like the following.

Write the Lambda function for approval requests

This Lambda function is invoked by the SNS notification. It sends a request that consists of an interactive message button to the incoming webhook you created earlier.  The following sample code sends the request to the incoming webhook. WEBHOOK_URL and SLACK_CHANNEL are the environment variables that hold values of the webhook URL that you created and the Slack channel where you want the interactive message button to appear.

# This function is invoked via SNS when the CodePipeline manual approval action starts.
# It will take the details from this approval notification and sent an interactive message to Slack that allows users to approve or cancel the deployment.

import os
import json
import logging
import urllib.parse

from base64 import b64decode
from urllib.request import Request, urlopen
from urllib.error import URLError, HTTPError

# This is passed as a plain-text environment variable for ease of demonstration.
# Consider encrypting the value with KMS or use an encrypted parameter in Parameter Store for production deployments.
SLACK_WEBHOOK_URL = os.environ['SLACK_WEBHOOK_URL']
SLACK_CHANNEL = os.environ['SLACK_CHANNEL']

logger = logging.getLogger()
logger.setLevel(logging.INFO)

def lambda_handler(event, context):
    print("Received event: " + json.dumps(event, indent=2))
    message = event["Records"][0]["Sns"]["Message"]
    
    data = json.loads(message) 
    token = data["approval"]["token"]
    codepipeline_name = data["approval"]["pipelineName"]
    
    slack_message = {
        "channel": SLACK_CHANNEL,
        "text": "Would you like to promote the build to production?",
        "attachments": [
            {
                "text": "Yes to deploy your build to production",
                "fallback": "You are unable to promote a build",
                "callback_id": "wopr_game",
                "color": "#3AA3E3",
                "attachment_type": "default",
                "actions": [
                    {
                        "name": "deployment",
                        "text": "Yes",
                        "style": "danger",
                        "type": "button",
                        "value": json.dumps({"approve": True, "codePipelineToken": token, "codePipelineName": codepipeline_name}),
                        "confirm": {
                            "title": "Are you sure?",
                            "text": "This will deploy the build to production",
                            "ok_text": "Yes",
                            "dismiss_text": "No"
                        }
                    },
                    {
                        "name": "deployment",
                        "text": "No",
                        "type": "button",
                        "value": json.dumps({"approve": False, "codePipelineToken": token, "codePipelineName": codepipeline_name})
                    }  
                ]
            }
        ]
    }

    req = Request(SLACK_WEBHOOK_URL, json.dumps(slack_message).encode('utf-8'))

    response = urlopen(req)
    response.read()
    
    return None

 

Create a SNS topic

Create a topic and then create a subscription that invokes the ApprovalRequester Lambda function. You can configure the manual approval action in the pipeline to send a message to this SNS topic when an approval action is required. When the pipeline reaches the approval stage, it sends a notification to this SNS topic. SNS publishes a notification to all of the subscribed endpoints. In this case, the Lambda function is the endpoint. Therefore, it invokes and executes the Lambda function. For information about how to create a SNS topic, see Create a Topic in the Amazon SNS Developer Guide.

Write the Lambda function for handling the interactive message button

This Lambda function is invoked by API Gateway. It receives the result of the interactive message button whether or not the build promotion was approved. If approved, an API call is made to CodePipeline to promote the build to the next environment. If not approved, the pipeline stops and does not move to the next stage.

The Lambda function code might look like the following. SLACK_VERIFICATION_TOKEN is the environment variable that contains your Slack verification token. You can find your verification token under Basic Information on Slack manage app page. When you scroll down, you will see App Credential. Verification token is found under the section.

# This function is triggered via API Gateway when a user acts on the Slack interactive message sent by approval_requester.py.

from urllib.parse import parse_qs
import json
import os
import boto3

SLACK_VERIFICATION_TOKEN = os.environ['SLACK_VERIFICATION_TOKEN']

#Triggered by API Gateway
#It kicks off a particular CodePipeline project
def lambda_handler(event, context):
	#print("Received event: " + json.dumps(event, indent=2))
	body = parse_qs(event['body'])
	payload = json.loads(body['payload'][0])

	# Validate Slack token
	if SLACK_VERIFICATION_TOKEN == payload['token']:
		send_slack_message(json.loads(payload['actions'][0]['value']))
		
		# This will replace the interactive message with a simple text response.
		# You can implement a more complex message update if you would like.
		return  {
			"isBase64Encoded": "false",
			"statusCode": 200,
			"body": "{\"text\": \"The approval has been processed\"}"
		}
	else:
		return  {
			"isBase64Encoded": "false",
			"statusCode": 403,
			"body": "{\"error\": \"This request does not include a vailid verification token.\"}"
		}


def send_slack_message(action_details):
	codepipeline_status = "Approved" if action_details["approve"] else "Rejected"
	codepipeline_name = action_details["codePipelineName"]
	token = action_details["codePipelineToken"] 

	client = boto3.client('codepipeline')
	response_approval = client.put_approval_result(
							pipelineName=codepipeline_name,
							stageName='Approval',
							actionName='ApprovalOrDeny',
							result={'summary':'','status':codepipeline_status},
							token=token)
	print(response_approval)

 

Create the API Gateway API

  1. In the Amazon API Gateway console, create a resource called InteractiveMessageHandler.
  2. Create a POST method.
    • For Integration type, choose Lambda Function.
    • Select Use Lambda Proxy integration.
    • From Lambda Region, choose a region.
    • In Lambda Function, type a name for your function.
  3.  Deploy to a stage.

For more information, see Getting Started with Amazon API Gateway in the Amazon API Developer Guide.

Now go back to your Slack application and enable interactive components.

To enable interactive components for the interactive message (Yes) button:

  1. Under Features, choose Interactive Components.
  2. Choose Enable Interactive Components.
  3. Type a request URL in the text box. Use the invoke URL in Amazon API Gateway that will be called when the approval button is clicked.

Now that all the pieces have been created, run the solution by checking in a code change to your CodeCommit repo. That will release the change through CodePipeline. When the CodePipeline comes to the approval stage, it will prompt to your Slack channel to see if you want to promote the build to your staging or production environment. Choose Yes and then see if your change was deployed to the environment.

Conclusion

That is it! You have now created a Slack ChatOps solution using AWS CodeCommit, AWS CodePipeline, AWS Lambda, Amazon API Gateway, and Amazon Simple Notification Service.

Now that you know how to do this Slack and CodePipeline integration, you can use the same method to interact with other AWS services using API Gateway and Lambda. You can also use Slack’s slash command to initiate an action from a Slack channel, rather than responding in the way demonstrated in this post.

C is to low level

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/c-is-too-low-level.html

I’m in danger of contradicting myself, after previously pointing out that x86 machine code is a high-level language, but this article claiming C is a not a low level language is bunk. C certainly has some problems, but it’s still the closest language to assembly. This is obvious by the fact it’s still the fastest compiled language. What we see is a typical academic out of touch with the real world.

The author makes the (wrong) observation that we’ve been stuck emulating the PDP-11 for the past 40 years. C was written for the PDP-11, and since then CPUs have been designed to make C run faster. The author imagines a different world, such as where CPU designers instead target something like LISP as their preferred language, or Erlang. This misunderstands the state of the market. CPUs do indeed supports lots of different abstractions, and C has evolved to accommodate this.


The author criticizes things like “out-of-order” execution which has lead to the Spectre sidechannel vulnerabilities. Out-of-order execution is necessary to make C run faster. The author claims instead that those resources should be spent on having more slower CPUs, with more threads. This sacrifices single-threaded performance in exchange for a lot more threads executing in parallel. The author cites Sparc Tx CPUs as his ideal processor.

But here’s the thing, the Sparc Tx was a failure. To be fair, it’s mostly a failure because most of the time, people wanted to run old C code instead of new Erlang code. But it was still a failure at running Erlang.

Time after time, engineers keep finding that “out-of-order”, single-threaded performance is still the winner. A good example is ARM processors for both mobile phones and servers. All the theory points to in-order CPUs as being better, but all the products are out-of-order, because this theory is wrong. The custom ARM cores from Apple and Qualcomm used in most high-end phones are so deeply out-of-order they give Intel CPUs competition. The same is true on the server front with the latest Qualcomm Centriq and Cavium ThunderX2 processors, deeply out of order supporting more than 100 instructions in flight.

The Cavium is especially telling. Its ThunderX CPU had 48 simple cores which was replaced with the ThunderX2 having 32 complex, deeply out-of-order cores. The performance increase was massive, even on multithread-friendly workloads. Every competitor to Intel’s dominance in the server space has learned the lesson from Sparc Tx: many wimpy cores is a failure, you need fewer beefy cores. Yes, they don’t need to be as beefy as Intel’s processors, but they need to be close.

Even Intel’s “Xeon Phi” custom chip learned this lesson. This is their GPU-like chip, running 60 cores with 512-bit wide “vector” (sic) instructions, designed for supercomputer applications. Its first version was purely in-order. Its current version is slightly out-of-order. It supports four threads and focuses on basic number crunching, so in-order cores seems to be the right approach, but Intel found in this case that out-of-order processing still provided a benefit. Practice is different than theory.

As an academic, the author of the above article focuses on abstractions. The criticism of C is that it has the wrong abstractions which are hard to optimize, and that if we instead expressed things in the right abstractions, it would be easier to optimize.

This is an intellectually compelling argument, but so far bunk.

The reason is that while the theoretical base language has issues, everyone programs using extensions to the language, like “intrinsics” (C ‘functions’ that map to assembly instructions). Programmers write libraries using these intrinsics, which then the rest of the normal programmers use. In other words, if your criticism is that C is not itself low level enough, it still provides the best access to low level capabilities.

Given that C can access new functionality in CPUs, CPU designers add new paradigms, from SIMD to transaction processing. In other words, while in the 1980s CPUs were designed to optimize C (stacks, scaled pointers), these days CPUs are designed to optimize tasks regardless of language.

The author of that article criticizes the memory/cache hierarchy, claiming it has problems. Yes, it has problems, but only compared to how well it normally works. The author praises the many simple cores/threads idea as hiding memory latency with little caching, but misses the point that caches also dramatically increase memory bandwidth. Intel processors are optimized to read a whopping 256 bits every clock cycle from L1 cache. Main memory bandwidth is orders of magnitude slower.

The author goes onto criticize cache coherency as a problem. C uses it, but other languages like Erlang don’t need it. But that’s largely due to the problems each languages solves. Erlang solves the problem where a large number of threads work on largely independent tasks, needing to send only small messages to each other across threads. The problems C solves is when you need many threads working on a huge, common set of data.

For example, consider the “intrusion prevention system”. Any thread can process any incoming packet that corresponds to any region of memory. There’s no practical way of solving this problem without a huge coherent cache. It doesn’t matter which language or abstractions you use, it’s the fundamental constraint of the problem being solved. RDMA is an important concept that’s moved from supercomputer applications to the data center, such as with memcached. Again, we have the problem of huge quantities (terabytes worth) shared among threads rather than small quantities (kilobytes).

The fundamental issue the author of the the paper is ignoring is decreasing marginal returns. Moore’s Law has gifted us more transistors than we can usefully use. We can’t apply those additional registers to just one thing, because the useful returns we get diminish.

For example, Intel CPUs have two hardware threads per core. That’s because there are good returns by adding a single additional thread. However, the usefulness of adding a third or fourth thread decreases. That’s why many CPUs have only two threads, or sometimes four threads, but no CPU has 16 threads per core.

You can apply the same discussion to any aspect of the CPU, from register count, to SIMD width, to cache size, to out-of-order depth, and so on. Rather than focusing on one of these things and increasing it to the extreme, CPU designers make each a bit larger every process tick that adds more transistors to the chip.

The same applies to cores. It’s why the “more simpler cores” strategy fails, because more cores have their own decreasing marginal returns. Instead of adding cores tied to limited memory bandwidth, it’s better to add more cache. Such cache already increases the size of the cores, so at some point it’s more effective to add a few out-of-order features to each core rather than more cores. And so on.

The question isn’t whether we can change this paradigm and radically redesign CPUs to match some academic’s view of the perfect abstraction. Instead, the goal is to find new uses for those additional transistors. For example, “message passing” is a useful abstraction in languages like Go and Erlang that’s often more useful than sharing memory. It’s implemented with shared memory and atomic instructions, but I can’t help but think it couldn’t better be done with direct hardware support.

Of course, as soon as they do that, it’ll become an intrinsic in C, then added to languages like Go and Erlang.

Summary

Academics live in an ideal world of abstractions, the rest of us live in practical reality. The reality is that vast majority of programmers work with the C family of languages (JavaScript, Go, etc.), whereas academics love the epiphanies they learned using other languages, especially function languages. CPUs are only superficially designed to run C and “PDP-11 compatibility”. Instead, they keep adding features to support other abstractions, abstractions available to C. They are driven by decreasing marginal returns — they would love to add new abstractions to the hardware because it’s a cheap way to make use of additional transitions. Academics are wrong believing that the entire system needs to be redesigned from scratch. Instead, they just need to come up with new abstractions CPU designers can add.

Monitoring for Everyone

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2018/05/23/monitoring-for-everyone/

Øredev – Carl Bergquist – Monitoring for Everyone What is monitoring?
What do the terms log, metric, and distributed tracing actually mean?
What makes a good alert?
Why should I care?
At a recent developer conference in Malmö, Sweden, I gave a presentation on monitoring and observability to discuss the high level concepts and common tools that are out there.
Monitoring and observability can easily become quite complex, but at the heart of it, we simply want to know how our systems are performing, and when performance drops – be able to find out why.

The Benefits of Side Projects

Post Syndicated from Bozho original https://techblog.bozho.net/the-benefits-of-side-projects/

Side projects are the things you do at home, after work, for your own “entertainment”, or to satisfy your desire to learn new stuff, in case your workplace doesn’t give you that opportunity (or at least not enough of it). Side projects are also a way to build stuff that you think is valuable but not necessarily “commercialisable”. Many side projects are open-sourced sooner or later and some of them contribute to the pool of tools at other people’s disposal.

I’ve outlined one recommendation about side projects before – do them with technologies that are new to you, so that you learn important things that will keep you better positioned in the software world.

But there are more benefits than that – serendipitous benefits, for example. And I’d like to tell some personal stories about that. I’ll focus on a few examples from my list of side projects to show how, through a sort-of butterfly effect, they helped shape my career.

The computoser project, no matter how cool algorithmic music composition, didn’t manage to have much of a long term impact. But it did teach me something apart from niche musical theory – how to read a bulk of scientific papers (mostly computer science) and understand them without being formally trained in the particular field. We’ll see how that was useful later.

Then there was the “State alerts” project – a website that scraped content from public institutions in my country (legislation, legislation proposals, decisions by regulators, new tenders, etc.), made them searchable, and “subscribable” – so that you get notified when a keyword of interest is mentioned in newly proposed legislation, for example. (I obviously subscribed for “information technologies” and “electronic”).

And that project turned out to have a significant impact on the following years. First, I chose a new technology to write it with – Scala. Which turned out to be of great use when I started working at TomTom, and on the 3rd day I was transferred to a Scala project, which was way cooler and much more complex than the original one I was hired for. It was a bit ironic, as my colleagues had just read that “I don’t like Scala” a few weeks earlier, but nevertheless, that was one of the most interesting projects I’ve worked on, and it went on for two years. Had I not known Scala, I’d probably be gone from TomTom much earlier (as the other project was restructured a few times), and I would not have learned many of the scalability, architecture and AWS lessons that I did learn there.

But the very same project had an even more important follow-up. Because if its “civic hacking” flavour, I was invited to join an informal group of developers (later officiated as an NGO) who create tools that are useful for society (something like MySociety.org). That group gathered regularly, discussed both tools and policies, and at some point we put up a list of policy priorities that we wanted to lobby policy makers. One of them was open source for the government, the other one was open data. As a result of our interaction with an interim government, we donated the official open data portal of my country, functioning to this day.

As a result of that, a few months later we got a proposal from the deputy prime minister’s office to “elect” one of the group for an advisor to the cabinet. And we decided that could be me. So I went for it and became advisor to the deputy prime minister. The job has nothing to do with anything one could imagine, and it was challenging and fascinating. We managed to pass legislation, including one that requires open source for custom projects, eID and open data. And all of that would not have been possible without my little side project.

As for my latest side project, LogSentinel – it became my current startup company. And not without help from the previous two mentioned above – the computer science paper reading was of great use when I was navigating the crypto papers landscape, and from the government job I not only gained invaluable legal knowledge, but I also “got” a co-founder.

Some other side projects died without much fanfare, and that’s fine. But the ones above shaped my “story” in a way that would not have been possible otherwise.

And I agree that such serendipitous chain of events could have happened without side projects – I could’ve gotten these opportunities by meeting someone at a bar (unlikely, but who knows). But we, as software engineers, are capable of tilting chance towards us by utilizing our skills. Side projects are our “extracurricular activities”, and they often lead to unpredictable, but rather positive chains of events. They would rarely be the only factor, but they are certainly great at unlocking potential.

The post The Benefits of Side Projects appeared first on Bozho's tech blog.

Solving Complex Ordering Challenges with Amazon SQS FIFO Queues

Post Syndicated from Christie Gifrin original https://aws.amazon.com/blogs/compute/solving-complex-ordering-challenges-with-amazon-sqs-fifo-queues/

Contributed by Shea Lutton, AWS Cloud Infrastructure Architect

Amazon Simple Queue Service (Amazon SQS) is a fully managed queuing service that helps decouple applications, distributed systems, and microservices to increase fault tolerance. SQS queues come in two distinct types:

  • Standard SQS queues are able to scale to enormous throughput with at-least-once delivery.
  • FIFO queues are designed to guarantee that messages are processed exactly once in the exact order that they are received and have a default rate of 300 transactions per second.

As customers explore SQS FIFO queues, they often have questions about how the behavior works when messages arrive and are consumed. This post walks through some common situations to identify the exact behavior that you can expect. It also covers the behavior of message groups in depth and explains why message groups are key to understanding how FIFO queues work.

The simple case

Suppose that you run a major auction platform where people buy and sell a wide range of products. Your platform requires that transactions from buyers and sellers get processed in exactly the order received. Here’s how a FIFO queue helps you keep all your transactions in one straight flow.

A seller currently is holding an auction for a laptop, and three different bids are received for the same price. Ties are awarded to the first bidder at that price so it is important to track which arrived first. Your auction platform receives the three bids and sends them to a FIFO queue before they are processed.

Now observe how messages leave the queue. When your consumer asks for a batch of up to 10 messages, SQS starts filling the batch with the oldest message (bid A1). It keeps filling until either the batch is full or the queue is empty. In this case, the batch contains the three messages and the queue is now empty. After a batch has left the queue, SQS considers that batch of messages to be “in-flight” until the consumer either deletes them or the batch’s visibility timer expires.

 

When you have a single consumer, this is easy to envision. The consumer gets a batch of messages (now in-flight), does its processing, and deletes the messages. That consumer is then ready to ask for the next batch of messages.

The critical thing to keep in mind is that SQS won’t release the next batch of messages until the first batch has been deleted. By adding more messages to the queue, you can see more interesting behaviors. Imagine that a burst of 11 bids is sent to your FIFO queue, with two bids for Auction A arriving last.

The FIFO queue now has at least two batches of messages in it. When your single consumer requests the first batch of 10 messages, it receives a batch starting with B1 and ending with A1. Later, after the first batch has been deleted, the consumer can get the second batch of messages containing the final A2 message from the queue.

Adding complexity with multiple message groups

A new challenge arises. Your auction platform is getting busier and your dev team added a number of new features. The combination of increased messages and extra processing time for the new features means that a single consumer is too slow. The solution is to scale to have more consumers and process messages in parallel.

To work in parallel, your team realized that only the messages related to a single auction must be kept in order. All transactions for Auction A need to be kept in order and so do all transactions for Auction B. But the two auctions are independent and it does not matter which auctions transactions are processed first.

FIFO can handle that case with a feature called message groups. Each transaction related to Auction A is placed by your producer into message group A, and so on. In the diagram below, Auction A and Auction B each received three bid transactions, with bid B1 arriving first. The FIFO queue always keeps transactions within a message group in the order in which they arrived.

How is this any different than earlier examples? The consumer now gets the messages ordered by message groups, all the B group messages followed by all the A group messages. Multiple message groups create the possibility of using multiple consumers, which I explain in a moment. If FIFO can’t fill up a batch of messages with a single message group, FIFO can place more than one message group in a batch of messages. But whenever possible, the queue gives you a full batch of messages from the same group.

The order of messages leaving a FIFO queue is governed by three rules:

  1. Return the oldest message where no other message in the same message group is currently in-flight.
  2. Return as many messages from the same message group as possible.
  3. If a message batch is still not full, go back to rule 1.

To see this behavior, add a second consumer and insert many more messages into the queue. For simplicity, the delete message action has been omitted in these diagrams but it is assumed that all messages in a batch are processed successfully by the consumer and the batch is properly deleted immediately after.

In this example, there are 11 Group A and 11 Group B transactions arriving in interleaved order and a second consumer has been added. Consumer 1 asks for a group of 10 messages and receives 10 Group A messages. Consumer 2 then asks for 10 messages but SQS knows that Group A is in flight, so it releases 10 Group B messages. The two consumers are now processing two batches of messages in parallel, speeding up throughput and then deleting their batches. When Consumer 1 requests the next batch of messages, it receives the remaining two messages, one from Group A and one from Group B.

Consider this nuanced detail from the example above. What would happen if Consumer 1 was on a faster server and processed its first batch of messages before Consumer 2 could mark its messages for deletion? See if you can predict the behavior before looking at the answer.

If Consumer 2 has not deleted its Group B messages yet when Consumer 1 asks for the next batch, then the FIFO queue considers Group B to still be in flight. It does not release any more Group B messages. Consumer 1 gets only the remaining Group A message. Later, after Consumer 2 has deleted its first batch, the remaining Group B message is released.

Conclusion

I hope this post answered your questions about how Amazon SQS FIFO queues work and why message groups are helpful. If you’re interested in exploring SQS FIFO queues further, here are a few ideas to get you started:

Analyze Apache Parquet optimized data using Amazon Kinesis Data Firehose, Amazon Athena, and Amazon Redshift

Post Syndicated from Roy Hasson original https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon-kinesis-data-firehose-amazon-athena-and-amazon-redshift/

Amazon Kinesis Data Firehose is the easiest way to capture and stream data into a data lake built on Amazon S3. This data can be anything—from AWS service logs like AWS CloudTrail log files, Amazon VPC Flow Logs, Application Load Balancer logs, and others. It can also be IoT events, game events, and much more. To efficiently query this data, a time-consuming ETL (extract, transform, and load) process is required to massage and convert the data to an optimal file format, which increases the time to insight. This situation is less than ideal, especially for real-time data that loses its value over time.

To solve this common challenge, Kinesis Data Firehose can now save data to Amazon S3 in Apache Parquet or Apache ORC format. These are optimized columnar formats that are highly recommended for best performance and cost-savings when querying data in S3. This feature directly benefits you if you use Amazon Athena, Amazon Redshift, AWS Glue, Amazon EMR, or any other big data tools that are available from the AWS Partner Network and through the open-source community.

Amazon Connect is a simple-to-use, cloud-based contact center service that makes it easy for any business to provide a great customer experience at a lower cost than common alternatives. Its open platform design enables easy integration with other systems. One of those systems is Amazon Kinesis—in particular, Kinesis Data Streams and Kinesis Data Firehose.

What’s really exciting is that you can now save events from Amazon Connect to S3 in Apache Parquet format. You can then perform analytics using Amazon Athena and Amazon Redshift Spectrum in real time, taking advantage of this key performance and cost optimization. Of course, Amazon Connect is only one example. This new capability opens the door for a great deal of opportunity, especially as organizations continue to build their data lakes.

Amazon Connect includes an array of analytics views in the Administrator dashboard. But you might want to run other types of analysis. In this post, I describe how to set up a data stream from Amazon Connect through Kinesis Data Streams and Kinesis Data Firehose and out to S3, and then perform analytics using Athena and Amazon Redshift Spectrum. I focus primarily on the Kinesis Data Firehose support for Parquet and its integration with the AWS Glue Data Catalog, Amazon Athena, and Amazon Redshift.

Solution overview

Here is how the solution is laid out:

 

 

The following sections walk you through each of these steps to set up the pipeline.

1. Define the schema

When Kinesis Data Firehose processes incoming events and converts the data to Parquet, it needs to know which schema to apply. The reason is that many times, incoming events contain all or some of the expected fields based on which values the producers are advertising. A typical process is to normalize the schema during a batch ETL job so that you end up with a consistent schema that can easily be understood and queried. Doing this introduces latency due to the nature of the batch process. To overcome this issue, Kinesis Data Firehose requires the schema to be defined in advance.

To see the available columns and structures, see Amazon Connect Agent Event Streams. For the purpose of simplicity, I opted to make all the columns of type String rather than create the nested structures. But you can definitely do that if you want.

The simplest way to define the schema is to create a table in the Amazon Athena console. Open the Athena console, and paste the following create table statement, substituting your own S3 bucket and prefix for where your event data will be stored. A Data Catalog database is a logical container that holds the different tables that you can create. The default database name shown here should already exist. If it doesn’t, you can create it or use another database that you’ve already created.

CREATE EXTERNAL TABLE default.kfhconnectblog (
  awsaccountid string,
  agentarn string,
  currentagentsnapshot string,
  eventid string,
  eventtimestamp string,
  eventtype string,
  instancearn string,
  previousagentsnapshot string,
  version string
)
STORED AS parquet
LOCATION 's3://your_bucket/kfhconnectblog/'
TBLPROPERTIES ("parquet.compression"="SNAPPY")

That’s all you have to do to prepare the schema for Kinesis Data Firehose.

2. Define the data streams

Next, you need to define the Kinesis data streams that will be used to stream the Amazon Connect events.  Open the Kinesis Data Streams console and create two streams.  You can configure them with only one shard each because you don’t have a lot of data right now.

3. Define the Kinesis Data Firehose delivery stream for Parquet

Let’s configure the Data Firehose delivery stream using the data stream as the source and Amazon S3 as the output. Start by opening the Kinesis Data Firehose console and creating a new data delivery stream. Give it a name, and associate it with the Kinesis data stream that you created in Step 2.

As shown in the following screenshot, enable Record format conversion (1) and choose Apache Parquet (2). As you can see, Apache ORC is also supported. Scroll down and provide the AWS Glue Data Catalog database name (3) and table names (4) that you created in Step 1. Choose Next.

To make things easier, the output S3 bucket and prefix fields are automatically populated using the values that you defined in the LOCATION parameter of the create table statement from Step 1. Pretty cool. Additionally, you have the option to save the raw events into another location as defined in the Source record S3 backup section. Don’t forget to add a trailing forward slash “ / “ so that Data Firehose creates the date partitions inside that prefix.

On the next page, in the S3 buffer conditions section, there is a note about configuring a large buffer size. The Parquet file format is highly efficient in how it stores and compresses data. Increasing the buffer size allows you to pack more rows into each output file, which is preferred and gives you the most benefit from Parquet.

Compression using Snappy is automatically enabled for both Parquet and ORC. You can modify the compression algorithm by using the Kinesis Data Firehose API and update the OutputFormatConfiguration.

Be sure to also enable Amazon CloudWatch Logs so that you can debug any issues that you might run into.

Lastly, finalize the creation of the Firehose delivery stream, and continue on to the next section.

4. Set up the Amazon Connect contact center

After setting up the Kinesis pipeline, you now need to set up a simple contact center in Amazon Connect. The Getting Started page provides clear instructions on how to set up your environment, acquire a phone number, and create an agent to accept calls.

After setting up the contact center, in the Amazon Connect console, choose your Instance Alias, and then choose Data Streaming. Under Agent Event, choose the Kinesis data stream that you created in Step 2, and then choose Save.

At this point, your pipeline is complete.  Agent events from Amazon Connect are generated as agents go about their day. Events are sent via Kinesis Data Streams to Kinesis Data Firehose, which converts the event data from JSON to Parquet and stores it in S3. Athena and Amazon Redshift Spectrum can simply query the data without any additional work.

So let’s generate some data. Go back into the Administrator console for your Amazon Connect contact center, and create an agent to handle incoming calls. In this example, I creatively named mine Agent One. After it is created, Agent One can get to work and log into their console and set their availability to Available so that they are ready to receive calls.

To make the data a bit more interesting, I also created a second agent, Agent Two. I then made some incoming and outgoing calls and caused some failures to occur, so I now have enough data available to analyze.

5. Analyze the data with Athena

Let’s open the Athena console and run some queries. One thing you’ll notice is that when we created the schema for the dataset, we defined some of the fields as Strings even though in the documentation they were complex structures.  The reason for doing that was simply to show some of the flexibility of Athena to be able to parse JSON data. However, you can define nested structures in your table schema so that Kinesis Data Firehose applies the appropriate schema to the Parquet file.

Let’s run the first query to see which agents have logged into the system.

The query might look complex, but it’s fairly straightforward:

WITH dataset AS (
  SELECT 
    from_iso8601_timestamp(eventtimestamp) AS event_ts,
    eventtype,
    -- CURRENT STATE
    json_extract_scalar(
      currentagentsnapshot,
      '$.agentstatus.name') AS current_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        currentagentsnapshot,
        '$.agentstatus.starttimestamp')) AS current_starttimestamp,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.firstname') AS current_firstname,
    json_extract_scalar(
      currentagentsnapshot,
      '$.configuration.lastname') AS current_lastname,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.username') AS current_username,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') AS               current_outboundqueue,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as current_inboundqueue,
    -- PREVIOUS STATE
    json_extract_scalar(
      previousagentsnapshot, 
      '$.agentstatus.name') as prev_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        previousagentsnapshot, 
       '$.agentstatus.starttimestamp')) as prev_starttimestamp,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.firstname') as prev_firstname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.lastname') as prev_lastname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.username') as prev_username,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') as current_outboundqueue,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as prev_inboundqueue
  from kfhconnectblog
  where eventtype <> 'HEART_BEAT'
)
SELECT
  current_status as status,
  current_username as username,
  event_ts
FROM dataset
WHERE eventtype = 'LOGIN' AND current_username <> ''
ORDER BY event_ts DESC

The query output looks something like this:

Here is another query that shows the sessions each of the agents engaged with. It tells us where they were incoming or outgoing, if they were completed, and where there were missed or failed calls.

WITH src AS (
  SELECT
     eventid,
     json_extract_scalar(currentagentsnapshot, '$.configuration.username') as username,
     cast(json_extract(currentagentsnapshot, '$.contacts') AS ARRAY(JSON)) as c,
     cast(json_extract(previousagentsnapshot, '$.contacts') AS ARRAY(JSON)) as p
  from kfhconnectblog
),
src2 AS (
  SELECT *
  FROM src CROSS JOIN UNNEST (c, p) AS contacts(c_item, p_item)
),
dataset AS (
SELECT 
  eventid,
  username,
  json_extract_scalar(c_item, '$.contactid') as c_contactid,
  json_extract_scalar(c_item, '$.channel') as c_channel,
  json_extract_scalar(c_item, '$.initiationmethod') as c_direction,
  json_extract_scalar(c_item, '$.queue.name') as c_queue,
  json_extract_scalar(c_item, '$.state') as c_state,
  from_iso8601_timestamp(json_extract_scalar(c_item, '$.statestarttimestamp')) as c_ts,
  
  json_extract_scalar(p_item, '$.contactid') as p_contactid,
  json_extract_scalar(p_item, '$.channel') as p_channel,
  json_extract_scalar(p_item, '$.initiationmethod') as p_direction,
  json_extract_scalar(p_item, '$.queue.name') as p_queue,
  json_extract_scalar(p_item, '$.state') as p_state,
  from_iso8601_timestamp(json_extract_scalar(p_item, '$.statestarttimestamp')) as p_ts
FROM src2
)
SELECT 
  username,
  c_channel as channel,
  c_direction as direction,
  p_state as prev_state,
  c_state as current_state,
  c_ts as current_ts,
  c_contactid as id
FROM dataset
WHERE c_contactid = p_contactid
ORDER BY id DESC, current_ts ASC

The query output looks similar to the following:

6. Analyze the data with Amazon Redshift Spectrum

With Amazon Redshift Spectrum, you can query data directly in S3 using your existing Amazon Redshift data warehouse cluster. Because the data is already in Parquet format, Redshift Spectrum gets the same great benefits that Athena does.

Here is a simple query to show querying the same data from Amazon Redshift. Note that to do this, you need to first create an external schema in Amazon Redshift that points to the AWS Glue Data Catalog.

SELECT 
  eventtype,
  json_extract_path_text(currentagentsnapshot,'agentstatus','name') AS current_status,
  json_extract_path_text(currentagentsnapshot, 'configuration','firstname') AS current_firstname,
  json_extract_path_text(currentagentsnapshot, 'configuration','lastname') AS current_lastname,
  json_extract_path_text(
    currentagentsnapshot,
    'configuration','routingprofile','defaultoutboundqueue','name') AS current_outboundqueue,
FROM default_schema.kfhconnectblog

The following shows the query output:

Summary

In this post, I showed you how to use Kinesis Data Firehose to ingest and convert data to columnar file format, enabling real-time analysis using Athena and Amazon Redshift. This great feature enables a level of optimization in both cost and performance that you need when storing and analyzing large amounts of data. This feature is equally important if you are investing in building data lakes on AWS.

 


Additional Reading

If you found this post useful, be sure to check out Analyzing VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight and Work with partitioned data in AWS Glue.


About the Author

Roy Hasson is a Global Business Development Manager for AWS Analytics. He works with customers around the globe to design solutions to meet their data processing, analytics and business intelligence needs. Roy is big Manchester United fan cheering his team on and hanging out with his family.

 

 

 

Securing Your Cryptocurrency

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/backing-up-your-cryptocurrency/

Securing Your Cryptocurrency

In our blog post on Tuesday, Cryptocurrency Security Challenges, we wrote about the two primary challenges faced by anyone interested in safely and profitably participating in the cryptocurrency economy: 1) make sure you’re dealing with reputable and ethical companies and services, and, 2) keep your cryptocurrency holdings safe and secure.

In this post, we’re going to focus on how to make sure you don’t lose any of your cryptocurrency holdings through accident, theft, or carelessness. You do that by backing up the keys needed to sell or trade your currencies.

$34 Billion in Lost Value

Of the 16.4 million bitcoins said to be in circulation in the middle of 2017, close to 3.8 million may have been lost because their owners no longer are able to claim their holdings. Based on today’s valuation, that could total as much as $34 billion dollars in lost value. And that’s just bitcoins. There are now over 1,500 different cryptocurrencies, and we don’t know how many of those have been misplaced or lost.



Now that some cryptocurrencies have reached (at least for now) staggering heights in value, it’s likely that owners will be more careful in keeping track of the keys needed to use their cryptocurrencies. For the ones already lost, however, the owners have been separated from their currencies just as surely as if they had thrown Benjamin Franklins and Grover Clevelands over the railing of a ship.

The Basics of Securing Your Cryptocurrencies

In our previous post, we reviewed how cryptocurrency keys work, and the common ways owners can keep track of them. A cryptocurrency owner needs two keys to use their currencies: a public key that can be shared with others is used to receive currency, and a private key that must be kept secure is used to spend or trade currency.

Many wallets and applications allow the user to require extra security to access them, such as a password, or iris, face, or thumb print scan. If one of these options is available in your wallets, take advantage of it. Beyond that, it’s essential to back up your wallet, either using the backup feature built into some applications and wallets, or manually backing up the data used by the wallet. When backing up, it’s a good idea to back up the entire wallet, as some wallets require additional private data to operate that might not be apparent.

No matter which backup method you use, it is important to back up often and have multiple backups, preferable in different locations. As with any valuable data, a 3-2-1 backup strategy is good to follow, which ensures that you’ll have a good backup copy if anything goes wrong with one or more copies of your data.

One more caveat, don’t reuse passwords. This applies to all of your accounts, but is especially important for something as critical as your finances. Don’t ever use the same password for more than one account. If security is breached on one of your accounts, someone could connect your name or ID with other accounts, and will attempt to use the password there, as well. Consider using a password manager such as LastPass or 1Password, which make creating and using complex and unique passwords easy no matter where you’re trying to sign in.

Approaches to Backing Up Your Cryptocurrency Keys

There are numerous ways to be sure your keys are backed up. Let’s take them one by one.

1. Automatic backups using a backup program

If you’re using a wallet program on your computer, for example, Bitcoin Core, it will store your keys, along with other information, in a file. For Bitcoin Core, that file is wallet.dat. Other currencies will use the same or a different file name and some give you the option to select a name for the wallet file.

To back up the wallet.dat or other wallet file, you might need to tell your backup program to explicitly back up that file. Users of Backblaze Backup don’t have to worry about configuring this, since by default, Backblaze Backup will back up all data files. You should determine where your particular cryptocurrency, wallet, or application stores your keys, and make sure the necessary file(s) are backed up if your backup program requires you to select which files are included in the backup.

Backblaze B2 is an option for those interested in low-cost and high security cloud storage of their cryptocurrency keys. Backblaze B2 supports 2-factor verification for account access, works with a number of apps that support automatic backups with encryption, error-recovery, and versioning, and offers an API and command-line interface (CLI), as well. The first 10GB of storage is free, which could be all one needs to store encrypted cryptocurrency keys.

2. Backing up by exporting keys to a file

Apps and wallets will let you export your keys from your app or wallet to a file. Once exported, your keys can be stored on a local drive, USB thumb drive, DAS, NAS, or in the cloud with any cloud storage or sync service you wish. Encrypting the file is strongly encouraged — more on that later. If you use 1Password or LastPass, or other secure notes program, you also could store your keys there.

3. Backing up by saving a mnemonic recovery seed

A mnemonic phrase, mnemonic recovery phrase, or mnemonic seed is a list of words that stores all the information needed to recover a cryptocurrency wallet. Many wallets will have the option to generate a mnemonic backup phrase, which can be written down on paper. If the user’s computer no longer works or their hard drive becomes corrupted, they can download the same wallet software again and use the mnemonic recovery phrase to restore their keys.

The phrase can be used by anyone to recover the keys, so it must be kept safe. Mnemonic phrases are an excellent way of backing up and storing cryptocurrency and so they are used by almost all wallets.

A mnemonic recovery seed is represented by a group of easy to remember words. For example:

eye female unfair moon genius pipe nuclear width dizzy forum cricket know expire purse laptop scale identify cube pause crucial day cigar noise receive

The above words represent the following seed:

0a5b25e1dab6039d22cd57469744499863962daba9d2844243fec 9c0313c1448d1a0b2cd9e230a78775556f9b514a8be45802c2808e fd449a20234e9262dfa69

These words have certain properties:

  • The first four letters are enough to unambiguously identify the word.
  • Similar words are avoided (such as: build and built).

Bitcoin and most other cryptocurrencies such as Litecoin, Ethereum, and others use mnemonic seeds that are 12 to 24 words long. Other currencies might use different length seeds.

4. Physical backups — Paper, Metal

Some cryptocurrency holders believe that their backup, or even all their cryptocurrency account information, should be stored entirely separately from the internet to avoid any risk of their information being compromised through hacks, exploits, or leaks. This type of storage is called “cold storage.” One method of cold storage involves printing out the keys to a piece of paper and then erasing any record of the keys from all computer systems. The keys can be entered into a program from the paper when needed, or scanned from a QR code printed on the paper.

Printed public and private keys

Printed public and private keys

Some who go to extremes suggest separating the mnemonic needed to access an account into individual pieces of paper and storing those pieces in different locations in the home or office, or even different geographical locations. Some say this is a bad idea since it could be possible to reconstruct the mnemonic from one or more pieces. How diligent you wish to be in protecting these codes is up to you.

Mnemonic recovery phrase booklet

Mnemonic recovery phrase booklet

There’s another option that could make you the envy of your friends. That’s the CryptoSteel wallet, which is a stainless steel metal case that comes with more than 250 stainless steel letter tiles engraved on each side. Codes and passwords are assembled manually from the supplied part-randomized set of tiles. Users are able to store up to 96 characters worth of confidential information. Cryptosteel claims to be fireproof, waterproof, and shock-proof.

image of a Cryptosteel cold storage device

Cryptosteel cold wallet

Of course, if you leave your Cryptosteel wallet in the pocket of a pair of ripped jeans that gets thrown out by the housekeeper, as happened to the character Russ Hanneman on the TV show Silicon Valley in last Sunday’s episode, then you’re out of luck. That fictional billionaire investor lost a USB drive with $300 million in cryptocoins. Let’s hope that doesn’t happen to you.

Encryption & Security

Whether you store your keys on your computer, an external disk, a USB drive, DAS, NAS, or in the cloud, you want to make sure that no one else can use those keys. The best way to handle that is to encrypt the backup.

With Backblaze Backup for Windows and Macintosh, your backups are encrypted in transmission to the cloud and on the backup server. Users have the option to add an additional level of security by adding a Personal Encryption Key (PEK), which secures their private key. Your cryptocurrency backup files are secure in the cloud. Using our web or mobile interface, previous versions of files can be accessed, as well.

Our object storage cloud offering, Backblaze B2, can be used with a variety of applications for Windows, Macintosh, and Linux. With B2, cryptocurrency users can choose whichever method of encryption they wish to use on their local computers and then upload their encrypted currency keys to the cloud. Depending on the client used, versioning and life-cycle rules can be applied to the stored files.

Other backup programs and systems provide some or all of these capabilities, as well. If you are backing up to a local drive, it is a good idea to encrypt the local backup, which is an option in some backup programs.

Address Security

Some experts recommend using a different address for each cryptocurrency transaction. Since the address is not the same as your wallet, this means that you are not creating a new wallet, but simply using a new identifier for people sending you cryptocurrency. Creating a new address is usually as easy as clicking a button in the wallet.

One of the chief advantages of using a different address for each transaction is anonymity. Each time you use an address, you put more information into the public ledger (blockchain) about where the currency came from or where it went. That means that over time, using the same address repeatedly could mean that someone could map your relationships, transactions, and incoming funds. The more you use that address, the more information someone can learn about you. For more on this topic, refer to Address reuse.

Note that a downside of using a paper wallet with a single key pair (type-0 non-deterministic wallet) is that it has the vulnerabilities listed above. Each transaction using that paper wallet will add to the public record of transactions associated with that address. Newer wallets, i.e. “deterministic” or those using mnemonic code words support multiple addresses and are now recommended.

There are other approaches to keeping your cryptocurrency transaction secure. Here are a couple of them.

Multi-signature

Multi-signature refers to requiring more than one key to authorize a transaction, much like requiring more than one key to open a safe. It is generally used to divide up responsibility for possession of cryptocurrency. Standard transactions could be called “single-signature transactions” because transfers require only one signature — from the owner of the private key associated with the currency address (public key). Some wallets and apps can be configured to require more than one signature, which means that a group of people, businesses, or other entities all must agree to trade in the cryptocurrencies.

Deep Cold Storage

Deep cold storage ensures the entire transaction process happens in an offline environment. There are typically three elements to deep cold storage.

First, the wallet and private key are generated offline, and the signing of transactions happens on a system not connected to the internet in any manner. This ensures it’s never exposed to a potentially compromised system or connection.

Second, details are secured with encryption to ensure that even if the wallet file ends up in the wrong hands, the information is protected.

Third, storage of the encrypted wallet file or paper wallet is generally at a location or facility that has restricted access, such as a safety deposit box at a bank.

Deep cold storage is used to safeguard a large individual cryptocurrency portfolio held for the long term, or for trustees holding cryptocurrency on behalf of others, and is possibly the safest method to ensure a crypto investment remains secure.

Keep Your Software Up to Date

You should always make sure that you are using the latest version of your app or wallet software, which includes important stability and security fixes. Installing updates for all other software on your computer or mobile device is also important to keep your wallet environment safer.

One Last Thing: Think About Your Testament

Your cryptocurrency funds can be lost forever if you don’t have a backup plan for your peers and family. If the location of your wallets or your passwords is not known by anyone when you are gone, there is no hope that your funds will ever be recovered. Taking a bit of time on these matters can make a huge difference.

To the Moon*

Are you comfortable with how you’re managing and backing up your cryptocurrency wallets and keys? Do you have a suggestion for keeping your cryptocurrencies safe that we missed above? Please let us know in the comments.


*To the Moon — Crypto slang for a currency that reaches an optimistic price projection.

The post Securing Your Cryptocurrency appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Supply-Chain Security

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

Earlier this month, the Pentagon stopped selling phones made by the Chinese companies ZTE and Huawei on military bases because they might be used to spy on their users.

It’s a legitimate fear, and perhaps a prudent action. But it’s just one instance of the much larger issue of securing our supply chains.

All of our computerized systems are deeply international, and we have no choice but to trust the companies and governments that touch those systems. And while we can ban a few specific products, services or companies, no country can isolate itself from potential foreign interference.

In this specific case, the Pentagon is concerned that the Chinese government demanded that ZTE and Huawei add “backdoors” to their phones that could be surreptitiously turned on by government spies or cause them to fail during some future political conflict. This tampering is possible because the software in these phones is incredibly complex. It’s relatively easy for programmers to hide these capabilities, and correspondingly difficult to detect them.

This isn’t the first time the United States has taken action against foreign software suspected to contain hidden features that can be used against us. Last December, President Trump signed into law a bill banning software from the Russian company Kaspersky from being used within the US government. In 2012, the focus was on Chinese-made Internet routers. Then, the House Intelligence Committee concluded: “Based on available classified and unclassified information, Huawei and ZTE cannot be trusted to be free of foreign state influence and thus pose a security threat to the United States and to our systems.”

Nor is the United States the only country worried about these threats. In 2014, China reportedly banned antivirus products from both Kaspersky and the US company Symantec, based on similar fears. In 2017, the Indian government identified 42 smartphone apps that China subverted. Back in 1997, the Israeli company Check Point was dogged by rumors that its government added backdoors into its products; other of that country’s tech companies have been suspected of the same thing. Even al-Qaeda was concerned; ten years ago, a sympathizer released the encryption software Mujahedeen Secrets, claimed to be free of Western influence and backdoors. If a country doesn’t trust another country, then it can’t trust that country’s computer products.

But this trust isn’t limited to the country where the company is based. We have to trust the country where the software is written — and the countries where all the components are manufactured. In 2016, researchers discovered that many different models of cheap Android phones were sending information back to China. The phones might be American-made, but the software was from China. In 2016, researchers demonstrated an even more devious technique, where a backdoor could be added at the computer chip level in the factory that made the chips ­ without the knowledge of, and undetectable by, the engineers who designed the chips in the first place. Pretty much every US technology company manufactures its hardware in countries such as Malaysia, Indonesia, China and Taiwan.

We also have to trust the programmers. Today’s large software programs are written by teams of hundreds of programmers scattered around the globe. Backdoors, put there by we-have-no-idea-who, have been discovered in Juniper firewalls and D-Link routers, both of which are US companies. In 2003, someone almost slipped a very clever backdoor into Linux. Think of how many countries’ citizens are writing software for Apple or Microsoft or Google.

We can go even farther down the rabbit hole. We have to trust the distribution systems for our hardware and software. Documents disclosed by Edward Snowden showed the National Security Agency installing backdoors into Cisco routers being shipped to the Syrian telephone company. There are fake apps in the Google Play store that eavesdrop on you. Russian hackers subverted the update mechanism of a popular brand of Ukrainian accounting software to spread the NotPetya malware.

In 2017, researchers demonstrated that a smartphone can be subverted by installing a malicious replacement screen.

I could go on. Supply-chain security is an incredibly complex problem. US-only design and manufacturing isn’t an option; the tech world is far too internationally interdependent for that. We can’t trust anyone, yet we have no choice but to trust everyone. Our phones, computers, software and cloud systems are touched by citizens of dozens of different countries, any one of whom could subvert them at the demand of their government. And just as Russia is penetrating the US power grid so they have that capability in the event of hostilities, many countries are almost certainly doing the same thing at the consumer level.

We don’t know whether the risk of Huawei and ZTE equipment is great enough to warrant the ban. We don’t know what classified intelligence the United States has, and what it implies. But we do know that this is just a minor fix for a much larger problem. It’s doubtful that this ban will have any real effect. Members of the military, and everyone else, can still buy the phones. They just can’t buy them on US military bases. And while the US might block the occasional merger or acquisition, or ban the occasional hardware or software product, we’re largely ignoring that larger issue. Solving it borders on somewhere between incredibly expensive and realistically impossible.

Perhaps someday, global norms and international treaties will render this sort of device-level tampering off-limits. But until then, all we can do is hope that this particular arms race doesn’t get too far out of control.

This essay previously appeared in the Washington Post.

Hutterer: X server pointer acceleration analysis

Post Syndicated from corbet original https://lwn.net/Articles/754103/rss

For those who are curious about the rather complex way in which X server
pointer acceleration works, Peter Hutterer has put together a four-part
series on the topic:
part 1,
part 2,
part 3,
and
part 4.
The input for the acceleration profile is a speed in mickeys, a threshold (in mickeys) and a max accel factor (unitless). Mickeys are a bit tricky. This means the acceleration is device-specific, the deltas for a mouse at 1000 dpi are 20% larger than the deltas for a mouse at 800 dpi (assuming same physical distance and speed)“.

Bell/TSN Letter to University Connects Site-Blocking Support to Students’ Futures

Post Syndicated from Andy original https://torrentfreak.com/bell-tsn-letter-to-university-connects-site-blocking-support-to-students-futures-180510/

In January, a coalition of Canadian companies called on local telecoms regulator CRTC to implement a website-blocking regime in Canada.

The coalition, Fairplay Canada, is a collection of organizations and companies with ties to the entertainment industries and includes Bell, Cineplex, Directors Guild of Canada, Maple Leaf Sports and Entertainment, Movie Theatre Association of Canada, and Rogers Media. Its stated aim is to address Canada’s online piracy problems.

While CTRC reviews FairPlay Canada’s plans, the coalition has been seeking to drum up support for the blocking regime, encouraging a diverse range of supporters to send submissions endorsing the project. Of course, building a united front among like-minded groups is nothing out of the ordinary but a situation just uncovered by Canadian law Professor Micheal Geist, one of the most vocal opponents of the proposed scheme, is bound to raise eyebrows.

Geist discovered a submission by Brian Hutchings, who works as Vice-President, Administration at Brock University in Ontario. Dated March 22, 2018, it notes that one of the university’s most sought-after programs is Sports Management, which helps Brock’s students to become “the lifeblood” of Canada’s sport and entertainment industries.

“Our University is deeply alarmed at how piracy is eroding an industry that employs so many of our co-op students and graduates. Piracy is a serious, pervasive threat that steals creativity, undermines investment in content development and threatens the survival of an industry that is also part of our national identity,” the submission reads.

“Brock ardently supports the FairPlay Canada coalition of more than 25 organizations involved in every aspect of Canada’s film, TV, radio, sports entertainment and music industries. Specifically, we support the coalition’s request that the CRTC introduce rules that would disable access in Canada to the most egregious piracy sites, similar to measures that have been taken in the UK, France and Australia. We are committed to assist the members of the coalition and the CRTC in eliminating the theft of digital content.”

The letter leaves no doubt that Brock University as a whole stands side-by-side with Fairplay Canada but according to a subsequent submission signed by Michelle Webber, President, Brock University Faculty Association (BUFA), nothing could be further from the truth.

Noting that BUFA unanimously supports the position of the Canadian Association of University Teachers which opposes the FairPlay proposal, Webber adds that BUFA stands in opposition to the submission by Brian Hutchings on behalf of Brock University.

“Vice President Hutching’s intervention was undertaken without consultation with the wider Brock University community, including faculty, librarians, and Senate; therefore, his submission should not be seen as indicative of the views of Brock University as a whole.”

BUFA goes on to stress the importance of an open Internet to researchers and educators while raising concerns that the blocking proposals could threaten the principles of net neutrality in Canada.

While the undermining of Hutching’s position is embarrassing enough, via access to information laws Geist has also been able to reveal the chain of events that prompted the Vice-President to write a letter of support on behalf of the whole university.

It began with an email sent by former Brock professor Cheri Bradish to Mark Milliere, TSN’s Senior Vice President and General Manager, with Hutchings copied in. The idea was to connect the pair, with the suggestion that supporting the site-blocking plan would help to mitigate the threat to “future work options” for students.

What followed was a direct email from Mark Milliere to Brian Hutchings, in which the former laid out the contributions his company makes to the university, while again suggesting that support for site-blocking would be in the long-term interests of students seeking employment in the industry.

On March 23, Milliere wrote to Hutchings again, thanking him for “a terrific letter” and stating that “If you need anything from TSN, just ask.”

This isn’t the first time that Bell has asked those beholden to the company to support its site-blocking plans.

Back in February it was revealed that the company had asked its own employees to participate in the site-blocking submission process, without necessarily revealing their affiliations with the company.

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

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.

[$] A kernel integrity subsystem update

Post Syndicated from jake original https://lwn.net/Articles/753276/rss

At the 2018 Linux Storage, Filesystem, and Memory-Management Summit, Mimi
Zohar gave a presentation in the
filesystem track on the Linux integrity subsystem. There is a lot
of talk that the integrity subsystem (usually referred to as “IMA”, which
is the integrity
measurement architecture
, though there is more to the subsystem) is
complex and
not documented well, she
said. So she wanted to give an overview of the subsystem and then to
discuss some filesystem-related concerns.

Pirate IPTV Blocking Case is No Slam Dunk Says Federal Court Judge

Post Syndicated from Andy original https://torrentfreak.com/pirate-iptv-blocking-case-is-no-slam-dunk-says-federal-court-judge-180502/

Last year, Hong Kong-based broadcaster Television Broadcasts Limited (TVB) applied for a blocking injunction against several unauthorized IPTV services.

Under the Copyright Act, the broadcaster asked the Federal Court to order ISPs including Telstra, Optus, Vocus, and TPG plus their subsidiaries to block access to seven Android-based services named as A1, BlueTV, EVPAD, FunTV, MoonBox, Unblock, and hTV5.

Unlike torrent site and streaming portal blocks granted earlier, it soon became clear that this case would present unique difficulties. TVB not only wants Internet locations (URLs, domains, IP addresses) related to the technical operation of the services blocked, but also hosting services akin to Google Play and Apple’s App Store that host the app.

Furthermore, it is far from clear whether China-focused live programming is eligible for copyright protection in Australia. If China had been a party to the 1961 Rome Convention for the Protection of Performers, Producers of Phonograms and Broadcasting Organisations, it would receive protection. As it stands, it does not.

That causes complications in respect of Section 115a of the Copyright Act which allows rightsholders to apply for an injunction to have “overseas online locations” blocked if they facilitate access to copyrighted content. Furthermore, the section requires that the “primary purpose” of the location is to infringe copyrights recognized in Australia. If it does not, then there’s no blocking option available.

“If most of what is occurring here is a reproduction of broadcasts that are not protected by copyright, then the primary purpose is not to facilitate copyright infringement,” Justice Nicholas said in April.

This morning TVB returned to Federal Court for a scheduled hearing. The ISPs were a no-show again, leaving the broadcaster’s legal team to battle it out with Justice Nicholas alone. According to details published by ComputerWorld, he isn’t making it easy for the overseas company.

The Judge put it to TVB that “the purpose of this system [the set-top boxes] is to make available a broadcast that’s not copyright protected in this country, in this country,” he said.

“If 10 per cent of the content was infringing content, how could you say the primary purpose is infringing copyright?” the Judge asked.

But despite the Judge’s reservations, TVB believes that the pirate IPTV services clearly infringe its rights, since alongside live programming, the devices also reproduce TVB movies which do receive protection in Australia. However, the company is also getting creative in an effort to sidestep the ‘live TV’ conundrum.

TVB counsel Julian Cooke told the Court that live TVB broadcasts are first reproduced on foreign servers from where they are communicated to set-top devices in Australia with a delay of between one and four minutes. This is a common feature of all pirate IPTV services which potentially calls into question the nature of the ‘live’ broadcasts. The same servers also carry recorded content too, he argued.

“Because the way the system is set up, it compounds itself … in a number of instances, a particular domain name, which we refer to as the portal target domain name, allows a communication path not just to live TV, but it’s also the communication path to other applications such as replay and video on demand,” Cooke said, as quoted by ZDNet.

Cooke told the Court that he wasn’t sure whether the threshold for “primary purpose” was set at 50% of infringing content but noted that the majority of the content available through the boxes is infringing and the nature of the servers is even more pronounced.

“It compounds the submission that the primary purpose of the online location which is the facilitating server is to facilitate the infringement of copyright using that communication path,” he said.

As TF predicted in our earlier coverage, TVB today got creative by highlighting other content that it does receive copyright protection for in Australia. Previously in the UK, the Premier League successfully stated that it owns copyright in the logos presented in a live broadcast.

This morning, Cooke told the court that TVB “literary works” – scripts used on news shows and subtitling services – receive copyright protection in Australia so urged the Court to consider the full package.

“If one had concerns about live TV, one shouldn’t based on the analysis we’ve done … if one adds that live TV infringements together with video on demand together with replay, there could be no doubt that the primary purpose of the online locations is to infringe copyright,” he said.

Due to the apparent complexity of the case, Justice Nicholas reserved his decision, telling TVB that his ruling could take a couple of months after receiving his “close attention.”

Last week, Village Roadshow and several major Hollywood studios won a blocking injunction against a different pirate IPTV service. HD Subs Plus delivers around 600 live premium channels plus hundreds of movies on demand, but the service will now be blocked by ISPs across Australia.

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