\nSo even if an attacker would be able to capture a snapshot of the Keccak state, it’s worthless within a single cycle.<\/p>\n
Raw data from modular entropy multiplication has certain properties (actually non-randomness) which are extremely useful:<\/p>\n
- \n
- The device is not rapidly changing the sort of numbers it puts out, so history can be used as a guide.<\/li>\n
- There is no special state stored in the modular entropy multiplier that could cause data to be different each clock cycle, other than on even\/odd cycles.<\/li>\n
- Bits further away are less correlated.<\/li>\n<\/ul>\n
This allows health monitoring on the raw data. Only when its in the expected range, it is passed to the Keccak sponge.<\/p>\n
Of course this also means you should not use the raw output for any cryptographic operations.
\nBut you have the freedom of accessing it directly to verify correct operation of the hardware or to apply different whitening functions,
\nbut right now SHA-3 is the best option!<\/p>\nHow it works<\/strong><\/h3>\n
The hardware derives entropy from thermal noise, like many random number generators. What divides it from other TRNGs is the\u00a0modular entropy multiplication<\/em><\/strong>\u00a0technique.<\/p>\n
Thermal noise of resistors is being amplified in an infinite loop to generate data \u2013 which is not totally random yet. By using modular entropy multiplication there is some correlation of adjacent bits in the stream.<\/p>\n
Health monitoring of important parameters of the raw data-stream is implemented in the devices driver, which then uses the SHA-3 hashing function for cryptographic whitening to produce true random numbers.<\/p>\n
![\"\"](\"https:\/\/leetronics.de\/wp-content\/uploads\/2018\/04\/trng-data-flow-1024x325.png\")
<\/a><\/p>\n<\/div>\nThere is no way to override the signal without being noticed by the driver. Of course it\u2019s possible to influence it a bit, but because we use modular entropy multiplication, this only makes the output slightly more random.<\/p>\n
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\u2019s the only way to constantly verify the hardware is working properly. And only then will it apply whitening with the SHA3 hashing function.<\/p>\n
It may sound daunting, but this is the key feature of this implementation, as the foreseeable (and very high)\u00a0level<\/em><\/strong>\u00a0of entropy enables the driver to monitor proper function of the device. It’s an essential feature for any trustworthy random number generator, which most available devices are lacking. Even when you can access the raw output – during operation you often don\u2019t find a way to monitor their operation.<\/p>\n
Key features<\/strong><\/h3>\n
- \n
- no firmware (that could be modified by an attacker)<\/li>\n
- using only stock components (COTS)<\/li>\n
- health monitor built into the driver<\/li>\n
- free software for GNU\/Linux, *BSD and Windows<\/li>\n
- open source hardware (OSHWA certified)<\/li>\n<\/ul>\n
Technical Data<\/b><\/h3>\n
- \n
- USB2.0 Interface<\/li>\n
- 30KByte\/s random output<\/li>\n
- Dimensions: 50mm (L) x 19mm (W) x 8mm (H)<\/li>\n
- Weight: 10g<\/li>\n
- Low power consumption of 8mA @ 5V<\/li>\n
- Made in Germany<\/li>\n<\/ul>\n
Resources<\/b><\/h3>\n
![\"Icon\"](\"https:\/\/leetronics.de\/wp-content\/plugins\/download-manager\/assets\/file-type-icons\/pdf.svg\")
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