The Infinite Noise TRNG is an affordable and secure true random number generator (TRNG). It’s based on a modular entropy multiplier technique that continuously loops over previous random output, gathering randomness from the noise of the hardware components along the way, to generate the next random output. This way it produces a constant, measurable level of entropy which is then whitened to produce true random numbers. The openness of the implementation makes it easy to inspect and verify, as all security hardware should be!
The hardware derives entropy from thermal noise, like many random number generators. What divides it from other TRNGs is the modular entropy multiplication technique.
Thermal noise of resistors is being amplified in an infinite loop to generate data – which is not totally random yet. By using modular entropy multiplication there is some correlation of adjacent bits in the stream.
Health monitoring of important parameters of the raw datastream is implemented in the devices driver, which then uses the SHA-3 hashing function for cryptographic whitening to produce true random numbers.
There is no way to override the signal without being noticed by the driver. Of course it’s possible to influence it a bit, but because we use modular entropy multiplication, this only makes the output slightly more random.
Since by definition there are no patterns in random data, how can you know the data coming from your entropy source was not spoofed? The Infinite Noise TRNG produces this predictable level of entropy, just because it’s the only way to constantly verify the hardware is working properly. And only then will it apply whitening with the SHA3 hashing function.
It may sound daunting, but this is the key feature of this implementation, as the foreseeable (and very high) level of entropy enables the driver to monitor proper function of the device. This is an essential feature for any trustworthy random number generator, which most devices unfortunately are missing. Even when you can access the raw output - during operation you often don’t find a way to monitor its operation.
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I analyzed the raw entropic data and it was in the .6 to .7 range of min entropy. The extractor is sha3 which is a good hash but single input extractors are not formally sound. The docs seem to say that the extraction ratio can be less that 1 which is not a good design practice. I put some details here - https://youtu.be/4PO2899Q1Wc