一种使用多条条件归约路径的轻量级巴雷特模乘故障检测方案
A Lightweight Fault-Detection Scheme for Barrett Modular Multiplication Using Multiple Conditional Reduction Paths
AI总结：
针对PQC与FHE中关键的巴雷特模乘单元易受故障攻击的问题，提出基于统计归约监测的轻量级故障检测方案，可高效检测各类故障且硬件开销极小。
AI中文摘要：
多项式乘法是基于格的后量子密码（PQC）和全同态加密（FHE）方案中对资源、时间和能耗要求最高的操作。Kyber、Dilithium等基于格的PQC方案已被标准化，而BGV、BFV、CKKS等基于格的FHE方案被公认为FHE领域的领先候选方案。巴雷特模乘（BMM）因硬件友好性和高效的模归约能力，被广泛应用于PQC和FHE硬件加速器中。然而，侧信道攻击（SCA）和硬件木马可能引入故意故障，老化及其他因素则会导致无意故障。这些故障可能针对PQC和FHE基础设施中最关键的组件之一——BMM单元，进而导致信息泄露，危及系统安全。本文采用统计归约监测（SRM）方法，以保护BMM单元免受此类对抗性条件影响。该方法硬件开销极小，同时能高效检测随机故障和突发故障。
英文摘要：
Polynomial multiplication is the most resource-, time-, and energy-critical operation in lattice-based Post-Quantum Cryptography (PQC) and Fully Homomorphic Encryption (FHE) schemes. Lattice-based PQC schemes such as Kyber and Dilithium have already been standardized, while lattice- based FHE schemes such as BGV, BFV, and CKKS are widely recognized as leading candidate in FHE area. Barrett Modular Multiplication (BMM) for polynomial multiplication is widely adopted in PQC and FHE hardware accelerators due to its hardware friendly nature and efficient modular reduction capabilities. However, Side-Channel Attacks (SCAs) and Hardware Trojans may introduce intentional faults, while aging and various other factors can cause unintentional faults. These faults may target the BM M unit, one of the most critical components of PQC and FHE infrastructures, potentially leading to information leakage and compromising system security. In this paper, we employ a Statistical Reduction Monitoring (SRM) method to protect the BM M unit against such adversarial conditions. The proposed approach incurs minimal hardware overhead while providing efficient detection of both random and bur
