Optimizing Linear Correctors: A Tight Output Min-Entropy Bound and Selection Technique
| Autor: | Grujić, Miloš, Verbauwhede, Ingrid |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | M. Gruji\'c and I. Verbauwhede, "Optimizing Linear Correctors: A Tight Output Min-Entropy Bound and Selection Technique," in IEEE Transactions on Information Forensics and Security, vol. 19, pp. 586-600, 2024 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/TIFS.2023.3326986 |
| Popis: | Post-processing of the raw bits produced by a true random number generator (TRNG) is always necessary when the entropy per bit is insufficient for security applications. In this paper, we derive a tight bound on the output min-entropy of the algorithmic post-processing module based on linear codes, known as linear correctors. Our bound is based on the codes' weight distributions, and we prove that it holds even for the real-world noise sources that produce independent but not identically distributed bits. Additionally, we present a method for identifying the optimal linear corrector for a given input min-entropy rate that maximizes the throughput of the post-processed bits while simultaneously achieving the needed security level. Our findings show that for an output min-entropy rate of $0.999$, the extraction efficiency of the linear correctors with the new bound can be up to $130.56\%$ higher when compared to the old bound, with an average improvement of $41.2\%$ over the entire input min-entropy range. On the other hand, the required min-entropy of the raw bits for the individual correctors can be reduced by up to $61.62\%$. Comment: Final version after the review process. Accepted for publication in IEEE Transactions on Information Forensics and Security. Corrected typos |
| Databáze: | arXiv |
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