EDBT 2026 Demo / reviewers in the wild / expert
Han-Bing Yu
dblp:329/5696
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cryptographic primitives and cryptanalysis › stream cipher
linear feedback shift register |
0.8 | 1 | 2024 | Predicting Truncated Galois Linear Feedback Shift Registers · IEEE Trans. Inf. Theory 2024 |
Cryptographic primitives and cryptanalysis
stream cipher cryptanalysis |
0.8 | 1 | 2024 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Predicting Truncated Galois Linear Feedback Shift RegistersabstractLinear 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. Theory | 1 |
| 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 |