English(EN) Bridging Number-Theoretic Primality Testing And Algorithmic Optimization: An Adaptive Cryptographic Framework

新框架融合数论与机器学习用于加密素性检验

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

一篇新研究论文提出了一种结合了数论、机器学习和优化算法的自适应加密框架。该框架旨在增强大数素性检验能力，这对于RSA、Diffie-Hellman和ECC等加密系统的安全性至关重要。研究还探讨了测试方法的理论基础、硬件优化以及量子安全熵源的使用，并为即使在素数分布有偏的情况下也能保证安全性提供了数学证明。 AI

影响这项研究通过机器学习和优化技术改进素性检验，有望增强当前加密系统的安全性。

排序理由该集群包含一篇在arXiv上发表的研究论文，详细介绍了一种新的加密素性检验框架。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Tran Minh Thanh, Vo Hoai Viet · 2026-10-23 04:00

Bridging Number-Theoretic Primality Testing And Algorithmic Optimization: An Adaptive Cryptographic Framework

arXiv:2610.30935v1 Announce Type: cross Abstract: The security of modern public-key cryptographic systems, such as RSA, Diffie-Hellman, and ECC, depends entirely on the computational intractability of the integer factorization and discrete logarithm problems. For these asymmetric…

本文如何被排名

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Bridging Number-Theoretic Primality Testing And Algorithmic Optimization: An Adaptive Cryptographic Framework

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