AI 中文总结
本研究分析同态加密（HE）对比特级故障的敏感性，聚焦CKKS方案，发现同态乘法是最易受错误影响的操作，揭示故障传播放大的鲁棒性漏洞，推动更具弹性的HE部署。
AI 中文摘要
同态加密（HE）支持无需解密的加密数据计算，是医疗、金融、政府等敏感领域隐私保护计算的核心基础。其安全性依赖噪声注入，这引入了固有的错误敏感性，引发了对HE系统容错性的担忧，因为软硬件诱导的故障可规避传统检测机制，导致数据静默损坏。本研究分析HE对比特级故障的敏感性，聚焦于AI和机器学习工作负载中广泛用于近似算术的CKKS（Cheon-Kim-Kim-Song）方案。研究发现同态乘法是实际HE流程中最易受错误影响的操作，表征了故障如何通过该操作传播和放大，揭示了关键的鲁棒性漏洞，推动了更具弹性的HE部署需求。
英文摘要
Homomorphic Encryption (HE) enables computation on encrypted data without decryption and is a key primitive for privacy-preserving computation in sensitive domains such as healthcare, finance, and government. Its security relies on noise injection, which introduces intrinsic error sensitivity and raises concerns about the fault tolerance of HE systems, as hardware- and software-induced faults can evade traditional detection mechanisms and lead to silent data corruption. In this work, we analyze the sensitivity of HE to bit-level faults, focusing on the CKKS (Cheon--Kim--Kim--Song) scheme widely used for approximate arithmetic in AI and machine learning workloads. We identify homomorphic multiplication as the most error-sensitive operation in practical HE pipelines and characterize how faults propagate and amplify through it, exposing a critical robustness vulnerability and motivating the need for more resilient HE deployments.
Comments3 pages, 3 figures
