Multi-Party Replicated Secret Sharing over a Ring with Applications to Privacy-Preserving Machine Learning
Multi-Party Replicated Secret Sharing over a Ring with Applications to Privacy-Preserving Machine Learning
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DOI：
10.56553/popets-2023-0035
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Alessandro N. Baccarini;Marina Blanton;Chen Yuan
中科院分区：
文献类型：
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作者：
Alessandro N. Baccarini;Marina Blanton;Chen Yuan
安全多方计算近年来在性能上有了显著提升，并且应用日益广泛。基于秘密共享的技术具有良好的性能，是隐私保护机器学习应用的热门选择。传统技术是在域上操作的，而针对环\(Z_2^k\)设计等效技术可以提高性能。在这项工作中，我们在诚实多数设置下为环开发了一套多方协议，从基本操作到更复杂的操作，目的是支持通用计算。我们证明，当使用不同数量的参与方实例化时，我们的技术比基于域的等效技术要快得多，并且与针对固定数量参与方定制设计的最先进技术性能相当甚至更好。我们在机器学习应用中评估了我们的技术，并表明它们具有良好的性能。
Secure multi-party computation has seen significant performance advances and increasing use in recent years. Techniques based on secret sharing offer attractive performance and are a popular choice for privacy-preserving machine learning applications. Traditional techniques operate over a field, while designing equivalent techniques for a ring Z_2^k can boost performance. In this work, we develop a suite of multi-party protocols for a ring in the honest majority setting starting from elementary operations to more complex with the goal of supporting general-purpose computation. We demonstrate that our techniques are substantially faster than their field-based equivalents when instantiated with a different number of parties and perform on par with or better than state-of-the-art techniques with designs customized for a fixed number of parties. We evaluate our techniques on machine learning applications and show that they offer attractive performance.