The last couple posts have been looking at the Cliff random number generator. Yesterday I posted about testing the generator with the DIEHARDER test suite, the successor to George Marsaglia’s DIEHARD battery of RNG tests. This morning I discovered something about the Cliff RNG which led to discovering something about DIEHARDER.The latter is more important: […]

# Category: RNG

## Testing Cliff RNG with DIEHARDER

My previous post introduced the Cliff random number generator. The post showed how to find starting seeds where the generator will start out by producing approximately equal numbers. Despite this flaw, the generator works well by some criteria. I produced a file of 100 million 32-bit integers by multiplying the output values [1], which were […]

## Fixed points of the Cliff random number generator

I ran across the Cliff random number generator yesterday. Given a starting value x0 in the open interval (0, 1), the generator proceeds by xn+1 = | 100 log(xn) mod 1 | for n > 0. The article linked to above says that this generator passes a test of randomness based on generating points on […]

## Using one RNG to sample another

Suppose you have two pseudorandom bit generators. They’re both fast, but not suitable for cryptographic use. How might you combine them into one generator that is suitable for cryptography? Coppersmith et al [1] had a simple but effective approach which they call the shrinking generator. The idea is to use one bit stream to sample […]

## Implementing the ChaCha RNG in Python

My previous post talked about the ChaCha random number generator and how Google is using it in a stream cipher for encryption on low-end devices. This post talks about how to implement ChaCha in pure Python. First of all, the only reason to implement ChaCha in pure Python is to play with it. It would […]