Han-Bing Yu

dblp:329/5696 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2024
0009-0001-7258-4768ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1 · 1 first-author · 1 since 2021Theory of computation · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Cryptographic primitives and cryptanalysis · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cryptographic primitives and cryptanalysis › stream cipher
linear feedback shift register
0.812024
Predicting Truncated Galois Linear Feedback Shift Registers · IEEE Trans. Inf. Theory 2024
Cryptographic primitives and cryptanalysis
stream cipher cryptanalysis
0.812024
Predicting Truncated Galois Linear Feedback Shift Registers · IEEE Trans. Inf. Theory 2024

Methods — techniques the papers use, named apart from their topics

resultant · 0.8lattice reduction · 0.8kannan's embedding · 0.8greatest common factor · 0.8
YearPublicationVenuePosition
2024 Predicting Truncated Galois Linear Feedback Shift Registers
abstract
Linear feedback shift registers (LFSRs) over integer residue rings are widely used to generate pseudorandom number, such as ZUC algorithm, truncated LCGs, truncated MRGs. Truncated Galois LFSRs are an important way to generate pseudorandom sequences. Methods to predict the whole sequences by the truncated sequences of the truncated Galois LFSRs are not only a crucial aspect of evaluating their security but also important concerns in their design. This paper studies the predictability of truncated Galois LFSRs. When the modulus and the state transition matrix are known, we first propose a lattice-based method to recover the initial state by the high-order truncated sequences, then discuss the condition that recovering the initial state by the low-order truncated sequences is meaningful, and finally solve the low-order case by transforming it into the high-order case. When the modulus and the state transition matrix are unknown, we generalize our recent work, using the resultant, the greatest common factor, and Kannan’s embedding technique in turn to recover the modulus, the characteristic polynomial, and the initial state. Moreover, we heuristically show that the state transition matrix can be successfully recovered only when all registers output sufficiently long truncated sequences. Experiments have verified the effectiveness of our methods.
Han-Bing Yu, Qun-Xiong Zheng
IEEE Trans. Inf. Theory1
2023 An improved method for predicting truncated multiple recursive generators with unknown parameters
Han-Bing Yu, Qun-Xiong Zheng, Jingguo Bi, Yu-Fei Duan, Jing-Wen Xue, Rong Cheng, Bai-Shun Sun
Des. Codes Cryptogr.1