Cryptographic random bit generators (RBGs), also known as random number generators (RNGs), require a noise source that produces digital outputs with some level of unpredictability, expressed as min-entropy. The SP800-90B_EntropyAssessment python package implements the min-entropy assessment methods included in the 2016 draft of Special Publication 800-90B.

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This code package requires 64-bit Python 2.7 or Python 3.

1. updated for second draft of SP 800-90B (January 2016)

##Basic Usage

There are three main files in this code package: iid_main.py, noniid_main.py, and restart.py. Brief usage descriptions are listed below. For further details, please refer to the user guide.

##Using iid_main.py The file iid_main.py calls all of the tests that determine whether or not the input file appears to contain independent and identically distributed (IID) samples, and if so, gives an entropy assessment. The program takes three arguments:

1. datafile: a binary file containing the samples to be tested.
2. bits_per_symbol: the number of bits required to represent the largest output symbol from the noise source. E.g., if the largest value is 12, this would be 4.

###Example > python iid_main.py truerand_1bit.bin 1 reading 1000000 bytes of data IID = True min-entropy = 0.995043

Don't forget to run the sanity check on a restart dataset using H_I = 0.995043

##Using noniid_main.py The file noniid_main.py calls all of the min-entropy estimation methods. The program requires two arguments:

1. datafile: a binary file containing the samples to be tested.
2. bits_per_symbol: the number of bits required to represent the largest output symbol from the noise source. E.g., if the largest value is 12, this would be 4.

###Example Non-IID estimators applied to same data as above:

> python noniid_main.py truerand_4bit.bin 4
reading 1000000 bytes of data
min-entropy = 3.70057

Don't forget to run the sanity check on a restart dataset using H_I = 3.70057

##Using restart.py The file restart.py performs the sanity checks on the restart dataset. The program requires three arguments:

1. datafile: a binary file containing the samples to be tested.
2. bits_per_symbol: the number of bits required to represent the largest output symbol from the noise source. E.g., if the largest value is 12, this would be 4.
3. H_I: initial entropy estimate obtained via iid_main.py or noniid_main.py.

###Example > python restart.py truerand_4bit.bin 4 3.70057 reading 1000000 bytes of data Passed the restart tests *** Final entropy estimate: 3.700570

##More Information For more information on using this code, such as optional arguments, see the user guide in this repository. For more information on the estimation methods, see SP 800-90B second draft.

###Contact Information This code is currently maintained by Kerry McKay and John Kelsey.