Learn tips and tricks using the less command-line text file viewer.
Why should clients get all the privacy? Give your servers some privacy too!
When people write privacy guides, for the most part they are written from the perspective of the client. Whether you are using HTTPS, blocking tracking cookies or going so far as to browse the internet over Tor, those privacy guides focus on helping end users protect themselves from the potentially malicious and spying web. Since many people who read Linux Journal sit on the other side of that equation—they run the servers that host those privacy-defeating services—system administrators also should step up and do their part to help user privacy. Although part of that just means making sure your services support TLS, in this article, I describe how to go one step further and make it possible for your users to use your services completely anonymously via Tor hidden services.How It Works
I'm not going to dive into the details of how Tor itself works so you can use the web anonymously—for those details, check out https://tor.eff.org. Tor hidden services work within the Tor network and allow you to register an internal, Tor-only service that gets its own .onion hostname. When visitors connect to the Tor network, Tor resolves those .onion addresses and directs you to the anonymous service sitting behind that name. Unlike with other services though, hidden services provide two-way anonymity. The server doesn't know the IP of the client, like with any service you access over Tor, but the client also doesn't know the IP of the server. This provides the ultimate in privacy since it's being protected on both sides.Warnings and Planning
As with setting up a Tor node itself, some planning is involved if you want to set up a Tor hidden service so you don't defeat Tor's anonymity via some operational mistake. There are a lot of rules both from an operational and security standpoint, so I recommend you read this excellent guide to find the latest best practices all in one place.
Without diving into all of those steps, I do want to list a few general-purpose guidelines here. First, you'll want to make sure that whatever service you are hosting is listening only on localhost (127.0.0.1) and isn't viewable via the regular internet. Otherwise, someone may be able to correlate your hidden service with the public one. Next, go through whatever service you are running and try to scrub specific identifying information from it. That means if you are hosting a web service, modify your web server so it doesn't report its software type or version, and if you are running a dynamic site, make sure whatever web applications you use don't report their versions either.Go to Full Article
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You don't need to be a data scientist to use Pandas for some basic analysis.
Traditionally, people who program in Python use the data types that come with the language, such as integers, strings, lists, tuples and dictionaries. Sure, you can create objects in Python, but those objects typically are built out of those fundamental data structures.
If you're a data scientist working with Pandas though, most of your time is spent with NumPy. NumPy might feel like a Python data structure, but it acts differently in many ways. That's not just because all of its operations work via vectors, but also because the underlying data is actually a C-style array. This makes NumPy extremely fast and efficient, consuming far less memory for a given array of numbers than traditional Python objects would do.
The thing is, NumPy is designed to be fast, but it's also a bit low level for some people. To get more functionality and a more flexible interface, many people use Pandas, a Python package that provides two basic wrappers around NumPy arrays: one-dimensional Series objects and two-dimensional Data Frame objects.
I often describe Pandas as "Excel within Python", in that you can perform all sorts of calculations as well as sort data, search through it and plot it.
For all of these reasons, it's no surprise that Pandas is a darling of the data science community. But here's the thing: you don't need to be a data scientist to enjoy Pandas. It has a lot of excellent functionality that's good for Python developers who otherwise would spend their time wrestling with lists, tuples and dictionaries.
So in this article, I describe some basic analysis that everyone can do with Pandas, regardless of whether you're a data scientist. If you ever work with CSV files (and you probably do), I definitely recommend thinking about using Pandas to open, read, analyze and even write to them. And although I don't cover it in this article, Pandas handles JSON and Excel very well too.Creating Data Frames
Although it's possible to create a data frame from scratch using Python data structures or NumPy arrays, it's more common in my experience to do so from a file. Fortunately, Pandas can load data from a variety of file formats.
Before you can do anything with Pandas, you have to load it. In a Jupyter notebook, do:%pylab inline import pandas as pd
For example, Python comes with a csv module that knows how to handle files in CSV (comma-separated value) format. But, then you need to iterate over the file and do something with each of those lines/rows. I often find it easier to use Pandas to work with such files. For example, here's a CSV file:a,b,c,d e,f,g,h "i,j",k,l,m n,o.p,q
You can turn this into a data frame with:Go to Full Article
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Last Call for Purism's Librem 5 Dev Kits, Git Protocol Version 2 Released, LXQt Version 0.13.0 Now Available and More
Purism announces last call for its Librem 5 dev kits. If you're interested in the hardware that will be the platform for the Librem 5 privacy-focused phones, place your order by June 1, 2018. The dev kit is $399, and it includes "screen, touchscreen, development mainboard, cabling, power supply and various sensors (free worldwide shipping)".
The Google Open Source Blog recently announced the release of Git protocol version 2. This release brings improvements to server-side reference filtering, easy extensibility for new features and simplified client handling of the http transport. See the full list of changes here.
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The Khronos Group yesterday announced "its engagement of Au-Zone Technologies to enable the NNEF (Neural Network Exchange Format) standard files to be used with leading machine learning training frameworks". See the Press Release for all the details on the Khronos Group and Au-Zone's development of open-source TensorFlow and Caffe2 Converters for NNEF.News Purism Git LXQt Desktop Red Hat Cloud Containers Debian Machine Learning