Scrambled Linear Pseudorandom Number Generators

F 2 -linear pseudorandom number generators are very popular due to their high speed, to the ease with which generators with a sizable state space can be created, and to their provable theoretical properties. However, they suffer from linear artifacts that show as failures in linearity-related statistical tests such as the binary-rank and the linear-complexity test. In this article, we give two new contributions. First, we introduce two new F 2 -linear transformations that have been handcrafted to have good statistical properties and at the same time to be programmable very efficiently on superscalar processors, or even directly in hardware. Then, we describe some scramblers , that is, nonlinear functions applied to the state array that reduce or delete the linear artifacts, and propose combinations of linear transformations and scramblers that give extremely fast pseudorandom number generators of high quality. A novelty in our approach is that we use ideas from the theory of filtered linear-feedback shift registers to prove some properties of our scramblers, rather than relying purely on heuristics. In the end, we provide simple, extremely fast generators that use a few hundred bits of memory, have provable properties, and pass strong statistical tests.

Method of Combining Cryptography and LDPC Coding for Enhanced Privacy

This paper describes a method of combining cryptographic encoding and low density parity check (LDPC) encoding for the purpose of enhancing privacy. This method uses pseudorandom number generators (PRNGs) to create parity check matrices that are constantly updated. The generated cyphertext is at least as private as a standard additive (XORing) cryptosystem, and also has error correcting capability. The eavesdropper, Eve, has the expanded burden of having to perform cryptanalysis and error correction simultaneously.

Varying PRNG to improve image cryptography implementation

Securing information became essential to exchange multimedia information safely. These exchanged data need to be transformed in a well-managed, secure, and reliable manner. This paper focuses on securing multimedia images via cryptography during transmission among users using an effective selection from several Pseudorandom Number Generators (PRNG). This paper implements several PRNG techniques involved within consecutive cryptoprocesses of substitution and transposition that have proven a secure process. In the study, different PRNGs are tested to encrypt images in forms of grayscale and colored RGB images compared to current similar approaches. The work experimentation is aiming at investigating and identifying suitability and reliability through security measures standard parameters. The research is showing proper PRNG selection as an attractive, significant work worth remarking for image cryptography.

Methods and Models for the Formation of Pseudo-Random Number Sequences Based on Cellular Automata

The chapter describes well-known models and implementation options for pseudorandom number generators based on cellular automata. Pseudorandom number generators based on synchronous and asynchronous cellular automata are briefly reviewed. Pseudorandom number generators based on one-dimensional and two-dimensional cellular automata, as well as using hybrid cellular automata, are described. New structures of pseudorandom number generators based on asynchronous cellular automata with a variable number of active cells are proposed. Testing of the proposed generators was carried out, which showed the high quality of the generators. Testing was conducted using graphical and statistical tests.

On pseudorandom number generators

<p class="Abstract">Statistical sampling and simulations produced by algorithms require fast random number generators; however, true random number generators are often too slow for the purpose, so pseudorandom number generators are usually more suitable. But choosing and using a pseudorandom number generator is no simple task; most pseudorandom number generators fail statistical tests. Default pseudorandom number generators offered by programming languages usually do not offer sufficient statistical properties. Testing random number generators so as to choose one for a project is essential to know its limitations and decide whether the choice fits the project’s objectives. However, this study presents a reproducible experiment that demonstrates that, despite all the contributions it made when it was first published, the popular NIST SP 800-22 statistical test suite as implemented in the software package is inadequate for testing generators.</p>