What I’ve learned about RSA Encryption and Large Prime Numbers – How To Generate

One of the ways in which I function, is to write down thoughts in this blog, that may seem clear to me at first, but which, once written down, require further thought and refinement.

I’ve written numerous times about Public Key Cryptography, in which the task needs to be solved, to generate 1024-bit prime numbers – or maybe even, larger prime numbers – And I had not paid much attention to the question, of how exactly to do that efficiently. Well only yesterday, I read a posting of another blogger, that inspired me. This blogger explained in common-sense language, that a probabilistic method exists to verify whether a large number is prime, that method being called “The Miller-Rabin Test”. And the blogger in question was named Antoine Prudhomme.

This blogger left out an important part in his exercise, in which he suggested some working Python code, but that would be needed if actual production grade-code was to generate large prime numbers for practical cryptography. He left out the eventual need, to perform more than just one type of test, because this blogger’s main goal was to explain the one method of testing, that was his posting subject.

I decided to modify his code, and to add a simple Fermat Test, simply because (in general,) to have two different probabilistic tests, reduces the chances of false success-stories, even further than Miller-Rabin would reduce those chances by itself. But Mr. Prudhomme already mentioned that the Fermat Test exists, which is much simpler than the Miller-Rabin Test. And, I added the step of just using a Seive, with the known prime numbers up to 65535, which is known not to be prime itself. The combined effect of added tests, which my code performs prior to applying Miller-Rabin, will also speed the execution of code, because I am applying the fastest tests first, to reduce the total number of times that the slower test needs to be applied, in case the candidate-number could in fact be prime, as not having been eliminated by the earlier, simpler tests. Further, I tested my code thoroughly last night, to make sure I’ve uploaded code that works.

Here is my initial, academic code:

http://dirkmittler.homeip.net/text/Generate_Prime_3.py

 

(Corrected 10/03/2018, 23h20 … )

(Updated 10/08/2018, 9h25 … )

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How To Install Yafaray Under Linux

One of the computing subtopics I dabble in, is the acquisition of 3D-graphics software. Therefore, I already have “Blender 2.78a”, which has its own built-in software-rendering engine, and I have several other rendering engines installed on my Linux-based computers.

Further, the rendering engines by themselves can be useless, unless they integrate well with a GUI (such as with Blender). And so one undertaking which I’ll typically reach with a given computer, is to install “Yafaray”, which used to be ‘Yafray’, which stood for ‘Yet Another Free Ray-Tracer’. If it’s installed properly, Blender can render its scenes, using Yafaray, but from within Blender.

Yafray used to be a much simpler piece of software to install than it has become. But I’m sure the effort I put into it this evening, will be well-worth it eventually. What I’m used to doing is to download a source-tree, and if it’s CMake-based, to run ‘cmake-gui‘ on it, to custom-pick my build options, and to go. But as it happens with Yafaray, this approach led to near chaos. What this did, was to compile all the source-code properly into libraries, but then to install those libraries to nonsensical locations within my system folders. One reason was the fact that a part of the project was to create Python 3 bindings, and another was the need for the Blender-integration, where modern Blender versions are based on Python 3. In any case I was sure to install all the build dependencies via my package-manager, but doing so was not enough to obtain working outcomes.

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