Full metadata record
DC FieldValueLanguage
dc.contributor.authorDeng, Lih-Yuanen_US
dc.contributor.authorShiau, Jyh-Jen Horngen_US
dc.contributor.authorLu, Henry Horng-Shingen_US
dc.contributor.authorBowman, Daleen_US
dc.date.accessioned2019-04-02T06:00:23Z-
dc.date.available2019-04-02T06:00:23Z-
dc.date.issued2018-08-01en_US
dc.identifier.issn0098-3500en_US
dc.identifier.urihttp://dx.doi.org/10.1145/3212673en_US
dc.identifier.urihttp://hdl.handle.net/11536/148215-
dc.description.abstractPseudo-random number generators (PRNGs) play an important role in both areas of computer simulation and computer security. Currently, there appears to be a huge divide between the types of PRNGs used in these two areas. For PRNGs in computer security applications, the security concern is extremely important. For PRNGs in computer simulation applications, the properties of high-dimensional equi-distribution, efficiency, long period-length, and portability are important. In recent years, there have been many PRNGs proposed in the area of computer simulation satisfying these nice properties. However, most of them are linear generators, thus sharing the same weakness in predictability. The major aim of this article is to propose a general class of secure generators, called SAFE (secure and fast encryption) generators, by properly "mixing" two baseline generators with the aforementioned properties to obtain a secure generator that would inherit these nice properties. Specifically, we propose applying a general mutual-shuffling method to certain linear generators, such as the currently most popular MT19937 generator and large-order multiple recursive generators, as well as outputting certain nonlinear transformations of the generated variates to construct secure PRNGS.en_US
dc.language.isoen_USen_US
dc.subjectMRGen_US
dc.subjectMT19937en_US
dc.subjectpseudo-random number generator (PRNG)en_US
dc.subjectnext number predictor (NNP)en_US
dc.subjectRC4en_US
dc.subjectmutual-shuffling generatoren_US
dc.titleSecure and Fast Encryption (SAFE) with Classical Random Number Generatorsen_US
dc.typeArticleen_US
dc.identifier.doi10.1145/3212673en_US
dc.identifier.journalACM TRANSACTIONS ON MATHEMATICAL SOFTWAREen_US
dc.citation.volume44en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:000445637100009en_US
dc.citation.woscount0en_US
Appears in Collections:Articles