PixCrypt：具有范围感知缓存的快速细粒度全同态加密
PixCrypt: Fast Fine-Grained FHE with Range-Aware Caching
- Worcester Polytechnic Institute（伍斯特理工学院）
- University of Washington（华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对细粒度加密数据计算开销大的问题，提出基于缓存与系数级操作的加速机制PixCrypt，支持CKKS、BFV和BGV，实现高达35倍加速并保持IND-CPA安全，提升FHE实用性。
AI中文摘要：
许多分析任务需要对加密数据进行安全计算。特别是像素级图像等细粒度数据需要更高的精度，因为每个像素都可能直接影响肿瘤分割和异常检测等任务的结果。虽然多方计算（MPC）具有交互性，差分隐私（DP）仅保护聚合值，而部分同态加密（PHE）缺乏乘法支持，它们都无法高效处理细粒度数据分析。全同态加密（FHE）独特地支持对加密像素进行任意操作，但计算成本高昂，给软件和硬件加速器都带来了重大挑战。我们提出了PixCrypt，一种用于细粒度全同态加密的基于缓存的加速机制。PixCrypt用缓存检索和系数级操作取代了昂贵的新密文生成，适用于CKKS、BFV和BGV方案，同时通过随机化重构确保密文不会重复。其线性噪声增长减少了对自举的需求并降低了NTT负载，提高了硬件加速器的效率。该设计实现了高达35倍的细粒度加密加速，并保持了IND-CPA（选择明文攻击下的不可区分性）安全性。在五个真实世界的像素级图像处理任务上的实验表明，PixCrypt显著提高了FHE在隐私保护分析中的实用性。
英文摘要：
Many analytics tasks require secure computation over encrypted data. In particular, fine-grained data such as pixel-level images require higher precision, as every pixel can directly affect outcomes in tasks like tumor segmentation and anomaly detection. While Multi-Party Computation (MPC) is interactive, Differential Privacy (DP) protects only aggregate values, and Partially Homomorphic Encryption (PHE) lacks multiplicative support, none of them can efficiently handle fine-grained data analytics. Fully Homomorphic Encryption (FHE) uniquely enables arbitrary operations on encrypted pixels but remains computationally expensive, posing significant challenges for both software and hardware accelerators. We present PixCrypt, a caching-based acceleration mechanism for fine-grained fully homomorphic encryption. PixCrypt replaces expensive fresh ciphertext generation with cache retrieval and coefficient-level operations across CKKS, BFV, and BGV, while randomized reconstruction ensures that ciphertexts do not repeat. Its linear noise growth reduces the need for bootstrapping and lowers NTT load, improving hardware accelerator efficiency. This design yields up to 35x faster fine-grained encryption and maintains IND-CPA (Indistinguishability under Chosen Plaintext Attack) security. Experiments on five real-world pixel-level image processing tasks show that PixCrypt significantly improves the practicality of FHE for privacy-preserving analytics.
