用领域知识改进格密码中的最短向量求解方法。
Domain-Informed Representation for Evolutionary Sieving in Integral and Module Lattices
- 将最短向量问题建模为带领域信息的遗传算法
- 在整数与模格上均实现更优求解性能
- 适合研究后量子密码与格算法优化者
传统密码学基于整数分解或离散对数等难题，但面临全规模量子计算机威胁。尽管尚属工程前沿，当前加密数据仍可能在未来被量子计算破解。为应对这一风险，现代抗量子密码的核心是短向量问题（SVP）。本文通过引入领域相关表示和交叉操作，改进了Laarhoven对Ajtai等人筛法的遗传算法处理方式，并自然拓展至模格场景，显著提升SVP求解效率。
原文摘要 · Abstract (English)
Traditional cryptography, rooted in problems, e.g., integer factorisation or discrete log, is inevitably vulnerable to a fully operational quantum computer. Although it remains an engineering frontier, the looming threat extends to encrypted data stored today, which could be decrypted in the future with quantum capabilities. To safeguard against this eventuality, the backbone of the modern quantum-safe cryptography is the Shortest Vector Problem (SVP). We enhance Laarhoven's treatment of Ajtai et al.'s sieving as a genetic algorithm (GA) for the SVP by incorporating domain-informed SVP representation and crossover while naturally extending application to the module lattices.
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