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Random Number Generation

A high quality random number generator (RNG) is critical for effective data encryption. In developing the LSET encryption algorithms, LSE Technologies has also developed a deterministic random number generator (DRNG) that not only performs extremely well on tests for assessing the quality and randomness of an RNG but is also extremely fast.

By combining this deterministic RNG with a number of simpler pseudo-random number generators, effectively true random number generation is achieved. This is the basis for our eTrueRNG™ algorithm. While it is mathematically possible for the eTrueRNG algorithm to produce a series of repeating numbers, it would require a minimum of six pseudo-random number generators to simultaneously produce the same six 32-bit values as simultaneously produced earlier in the random number generation sequence. At this point, two continuously changing entropy data tables (a minumum of 192 bytes) would also have to contain the exact same data (in the same table locations) as they contained at the same previous point in time for the output data to repeat. The improbability of this happening makes for effectively true random number generation for most applications.

The first member of our family of eTrueRNG algorithm based RNGs is the eTRNG C object code module for Linux. By incorporating our eTRNG code into your application, you no longer have to rely on the system random number generator with its questionable entropy levels and uncertain response times or suffer what can be a large expense for a hardware true RNG. Using 16MB strings of random numbers, eTRNG passes the Diehard and NIST 800-22 test suites (97% to 100% scores on 800-22). The ENT tests typically report entropy better than 7.9999 bits per byte, compression reduction of 0%, Monte Carlo error values between 0.01% and 0.05% and arithmetic mean within 0.01 of random.

eTRNG Features & Benefits
Feature Benefit
Random and deterministic modes Deterministic mode based on user provided seed data allows for repeatable random number strings when required by the application.
Single and multithread versions In random mode, running the RNG function and Entropy Generation function on different cores can provide increased throughput (depending on system loading). This also provides a higher degree of entropy in the data feeding the DRNG since the Entropy Generation runs asynchronously to the RNG function.
Single word and block generation Single word generation can be for used for encryption keys, password generation, feeding /dev/rand, etc. Block generation allows a virtually unlimited length string of random numbers to be generated in smaller blocks.
High performance Typical block generation rates of 45Mbytes/sec1.
Multiple entropy sources System performance and loading information can be used for seed generation and continuous entropy data generation.
Highly configurable A number of options are provided to tailor performance and degree of randomness to the application’s requirements.
1. Based on an Intel i7-2620M at 2.7Ghz.

The eTRNG C object code module is available in two versions, Basic and Advanced for different levels of price/performance. The Windows version of eTRNG should be avialable by September 1, 2016. Please contact us if you would like to be notified when it is available.

eTRNG Version Comparison
Multi-threaded option N Y
Random & Deterministic modes Y Y
Initialization with system entropy option Y Y
RNG using continuous system entropy option N Y
Price - non-commercial use1 $175 USD $295 USD
Price - commercial use2 $495 USD $995 USD
1. For research and educational use only, the eTRNG license agreement prohibits use in a commerical product.
2. The eTRNG license agreement provides a royalty free, perpetual use license for use in a single product. Please contact us for pricing for use in multiple products.

Detailed information on the eTRNG algorithm is available under NDA. Please contact us for summary test results, a copy of the eTRNG integration guide or for information on purchasing eTRNG.

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