为隐私保护机器学习设计了高效稀疏矩阵乘法算法。
Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning
- 提出专用安全稀疏矩阵乘法，避免稠密表示的内存问题。
- 通信开销降低最高达1000倍，适用于真实规模问题。
- 基于真实稀疏数据特性，减少对公开信息的依赖。
为保护数据隐私，多方计算（MPC）可在私有数据上执行机器学习算法。然而，现有MPC框架缺乏对稀疏数据的优化操作，难以处理稀疏数据的机器学习应用（如推荐系统或基因组学）。即使在明文下，这些应用涉及高维稀疏数据，若无稀疏优化将因内存过大而无法处理。由于矩阵乘法是机器学习的核心组件，本文提出专门用于秘密共享稀疏矩阵的安全乘法算法。相比经典稠密矩阵乘法，我们的稀疏算法在避免稠密数据表示带来的内存问题的同时，显著降低通信开销（最高达×1000）。我们在两个稠密乘法不适用的机器学习应用中验证了算法有效性。最后，借鉴真实稀疏数据特征，提出了三种技术，最小化安全稀疏算法所需的公开知识。
原文摘要 · Abstract (English)
To preserve data privacy, multi-party computation (MPC) enables executing Machine Learning (ML) algorithms on private data. However, MPC frameworks do not include optimized operations on sparse data. This absence makes them unsuitable for ML applications involving sparse data; e.g., recommender systems or genomics. Even in plaintext, such applications involve high-dimensional sparse data, that cannot be processed without sparsity-related optimizations due to prohibitively large memory requirements. Since matrix multiplication is a central building block of ML algorithms, our work proposes dedicated MPC algorithms to multiply secret-shared sparse matrices. Our sparse algorithms have several advantages over secure dense matrix multiplications (i.e., the classic multiplication). On the one hand, they avoid the memory issues caused by the "dense" data representation of dense multiplications. On the other hand, our algorithms can significantly reduce communication costs (up to $\times1000$) for realistic problem sizes. We validate our algorithms in two machine learning applications where dense matrix multiplications are impractical. Finally, we take inspiration from real-world sparse data properties to build 3 techniques minimizing the public knowledge necessary to secure sparse algorithms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。