A newer version of this document is available. Customers should click here to go to the newest version.
A.1. Random Number Generator Library
The Random Number Generator Library and Cryptography
The use of these pseudo-random number generator (PRNG) algorithms are not recommended for cryptographic purposes. The PRNGs included in this library are not cryptographically-secure pseudo-random number generators (CSPRNGs) and should not be used for cryptography. CSPRNG algorithms are designed so that no polynomial-time algorithm (PTA) can compute or predict the next bit in the pseudo-random sequence, nor is there a PTA that can predict past values of the CSPRNG; these algorithms do not achieve this purpose. Additionally, these algorithms have not been reviewed nor are they recommended for use as a PRNG component of a CSPRNG, even if the input values are from a non-deterministic entropy source with an appropriate entropy extractor.
|Value distribution||Value type||Value range||Generation method|
|Uniform||Integer||[-2³¹, 2³¹-1]||Tausworthe Generator|
|Floating point||[0, 1) (non-inclusive)||Tausworthe Generator|
|Gaussian||Floating point||[0, 1)||Central limit theorem (CLT) (Default)|
The header file is self-documented. You can review the header file to learn how to use the random number generator library in your component.
Random Number Object Declarations
- Uniform distribution integer random number
static RNG_Uniform<int> <object_name>(<seed_value>)
- Uniform distribution floating point random number
static RNG_Uniform<float> <object_name>(<seed_value>)
- Gaussian distribution floating point random number (CLT method)
static RNG_Gaussian<float> <object_name>(<seed_value>)
static RNG_Gaussian<float, ihc::GAUSSIAN_CLT> <object_name>(<seed_value>)
- Gaussian distribution floating point random number (Box-Muller method)
static RNG_Gaussian<float, ihc::GAUSSIAN_BOX_MULLER> <object_name>(<seed_value>)
Did you find the information on this page useful?