构建AI攻破后量子密码LWE的开源数据集，助力算法研究
TAPAS: Datasets for Learning the Learning with Errors Problem
- 提供多个LWE参数设置下的现成数据集
- 支持AI模型直接训练，加速攻击算法研发
- 适合后量子密码与机器学习交叉研究者使用
人工智能驱动的攻击在某些参数设置下，已可媲美甚至超越传统的经典攻击方法，对学习误差（LWE）这一后量子密码的核心难题构成威胁。然而，由于缺乏易获取的数据资源，人工智能研究人员难以深入探索和优化此类攻击。生成用于训练的LWE数据既耗时又需深厚领域知识。为此，本文提出TAPAS数据集——一个用于人工智能系统分析后量子密码的工具包，覆盖多种LWE设置，可被人工智能研究者直接使用，快速验证新攻击策略。本工作详细说明了数据集构建过程，建立了攻击性能基准线，并指明了未来研究方向。
原文摘要 · Abstract (English)
AI-powered attacks on Learning with Errors (LWE), an important hard math problem in post-quantum cryptography, rival or outperform "classical" attacks on LWE under certain parameter settings. Despite the promise of this approach, a dearth of accessible data limits AI practitioners' ability to study and improve these attacks. Creating LWE data for AI model training is time- and compute-intensive and requires significant domain expertise. To fill this gap and accelerate AI research on LWE attacks, we propose the TAPAS datasets, a Toolkit for Analysis of Post-quantum cryptography using AI Systems. These datasets cover several LWE settings and can be used off-the-shelf by AI practitioners to prototype new approaches to cracking LWE. This work documents TAPAS dataset creation, establishes attack performance baselines, and lays out directions for future work.
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