用加密技术保护隐私，让推荐系统更高效
Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption
- 将压缩稀疏行格式与全同态加密结合，处理稀疏评分矩阵
- 加密环境下实现高精度推荐，通信开销最低
- 适合需要保护用户隐私的推荐系统开发者
在数据驱动的时代，推荐系统虽能个性化用户体验，却依赖敏感数据，引发隐私担忧。全同态加密（FHE）可保障系统安全，但将FHE应用于推荐系统时，如何高效处理固有的大而稀疏的用户-物品评分矩阵仍是重大挑战。FHE运算计算量大，若对各类稀疏矩阵进行直接处理，成本将难以承受。此外，加密域中多方间的通信开销也是关键问题。本文提出一种新方法：将压缩稀疏行（CSR）表示与基于FHE的矩阵分解相结合，在加密域中高效处理矩阵稀疏性，同时最小化通信成本。实验结果表明，该方法在加密数据下实现了高推荐精度，并达到最低通信开销，有效保护用户隐私。
原文摘要 · Abstract (English)
In today's data-driven world, recommendation systems personalize user experiences across industries but rely on sensitive data, raising privacy concerns. Fully homomorphic encryption (FHE) can secure these systems, but a significant challenge in applying FHE to recommendation systems is efficiently handling the inherently large and sparse user-item rating matrices. FHE operations are computationally intensive, and naively processing various sparse matrices in recommendation systems would be prohibitively expensive. Additionally, the communication overhead between parties remains a critical concern in encrypted domains. We propose a novel approach combining Compressed Sparse Row (CSR) representation with FHE-based matrix factorization that efficiently handles matrix sparsity in the encrypted domain while minimizing communication costs. Our experimental results demonstrate high recommendation accuracy with encrypted data while achieving the lowest communication costs, effectively preserving user privacy.
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