Paper 2022/1134 Original: eprint.iacr.org/2022/1134 ↗

An improved method for predicting truncated multiple recursive generators with unknown parameters

Han-Bing Yu
Qun-Xiong Zheng
Yi-Jian Liu
Jing-Guo Bi
Yu-Fei Duan
Jing-Wen Xue
You Wu
Yue Cao
Rong Cheng
Lin Wang
Bai-Shun Sun
Abstract

Multiple recursive generators are an important class of pseudorandom number generators which are widely used in cryptography. The predictability of truncated sequences that predict the whole sequences by the truncated high-order bits of the sequences is not only a crucial aspect of evaluating the security of pseudorandom number generators but also serves an important role in the design of pseudorandom number generators. This paper improves the work of Sun et al on the predictability of truncated multiple recursive generators with unknown parameters. Given a few truncated digits of high-order bits output by a multiple recursive generator, we adopt the resultant, the Chinese Remainder Theorem and the idea of recovering $p$-adic coordinates of the coefficients layer by layer, and Kannan's embedding technique to recover the modulus, the coefficients and the initial state, respectively. Experimental results show that our new method is superior to that of the work of Sun et al, no matter in terms of the running time or the number of truncated digits required.

Metadata
Available format(s)
PDF
Category
Attacks and cryptanalysis
Publication info
Preprint.
History
2022-08-31: last updated
2022-08-31: published
Short URL
https://ia.cr/2022/1134
License
https://creativecommons.org/licenses/by/4.0/

BibTeX

@misc{cryptoeprint:2022/1134,
      author = {Han-Bing Yu and Qun-Xiong Zheng and Yi-Jian Liu and Jing-Guo Bi and Yu-Fei Duan and Jing-Wen Xue and You Wu and Yue Cao and Rong Cheng and Lin Wang and Bai-Shun Sun},
      title = {An improved method for predicting truncated multiple recursive generators with unknown parameters},
      howpublished = {Cryptology {ePrint} Archive, Paper 2022/1134},
      year = {2022},
      url = {https://eprint.iacr.org/2022/1134}
}
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