Random number generator (RNG) is the foundation and core of modern cryptography. The random number generated by RNG provides basic security for many cryptographic applications, such as cryptographic algorithms and security protocols. With the development of mobile Internet, Internet of things and other technologies,the traditional hardware-based random number generator has the problems of difficult hardware update and high development cost, which limits its application scope. Therefore, software RNG (SRNG) is usually used in computers, mobile terminals and other devices to provide random number services. At present, Linux, Android, Windows, and other typical operating system platforms have their own SRNG, providing software-based random number generation services. The existing research focuses on the lack of entropy of the entropy source and the internal state leakage of the post-processing module, which is the main problem affecting the random number service quality of SRNG. Therefore, a software random number generator with entropy monitoring (entropy monitoring SRNG, EM-SRNG) is designed and implemented in this paper, which uses high-precision nanosecond system clock as non-physical entropy source. The online entropy monitoring module can continuously detect the entropy of the unprocessed data when the generator is running, and call the post-processing module to improve the statistical characteristics of the data when the entropy is insufficient. In addition, the post-processing module of EM-SRNG can choose two post-processing extension algorithms designed based on SM3 and SM4 cryptography algorithms to ensure the forward/backward security of the internal state of the generator. By comparing the EM-SRNG and the Linux random number generator (LRNG, one of the current mainstream SRNGs), the experimental results show that, in terms of security, through SP 800-90B test, it is found that the output quality of EM-SRNG is equal to the data quality provided by LRNG dev/random, but slightly better than that provided by LRNG dev/random, with the minimum entropy of about 0.94/bit per bit; in terms of rate, the data generation rate of EM-SRNG is about 4 orders of magnitude higher than that of LRNG dev/random, but because the 90B statistical suite is embedded in the structure for online entropy estimation, the speed of EM-SRNG is slower than that of LRNG dev/urandom, which is about 4 Mbps.
LIU Pan
,
CHEN Tianyu
,
LÜ Na
,
MA Yuan
,
JING Jiwu
. A software random number generator with entropy monitoring function[J]. Journal of University of Chinese Academy of Sciences, 2020
, 37(6)
: 835
-847
.
DOI: 10.7523/j.issn.2095-6134.2020.06.016
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