环上主动安全MPC的新原语及其在私有机器学习中的应用

I. Damgård, Daniel E. Escudero, T. Frederiksen, Marcel Keller, Peter Scholl, Nikolaj Volgushev
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引用次数: 112

摘要

在CRYPTO 2018上,Cramer等人提出了SPDZ2k,这是一种新的基于秘密共享的协议,用于针对不诚实的多数进行主动安全的多方计算,该协议通过环而不是字段工作。他们的协议比在田野上工作的竞争方案使用更多的通信。然而,在实现方面,他们的方法允许使用本地32位或64位CPU操作来执行算术运算,而不是对一个大素数取模。因此,作者推测,增加的通信将被实现效率的提高所弥补。在这项工作中,我们肯定地回答了他们的猜想。为此,我们实现了他们的方案,并设计和实现了新的有效协议,用于环上的相等性测试、比较和截断。我们进一步表明,这些操作在机器学习领域得到了应用,并且确实显著优于基于该领域的竞争对手。特别地,我们实现和基准无关算法的决策树和支持向量机(SVM)评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning
At CRYPTO 2018 Cramer et al. presented SPDZ2k , a new secret-sharing based protocol for actively secure multi-party computation against a dishonest majority, that works over rings instead of fields. Their protocol uses slightly more communication than competitive schemes working over fields. However, implementation-wise, their approach allows for arithmetic to be carried out using native 32 or 64-bit CPU operations rather than modulo a large prime. The authors thus conjectured that the increased communication would be more than made up for by the increased efficiency of implementations. In this work we answer their conjecture in the affirmative. We do so by implementing their scheme, and designing and implementing new efficient protocols for equality test, comparison, and truncation over rings. We further show that these operations find application in the machine learning domain, and indeed significantly outperform their field-based competitors. In particular, we implement and benchmark oblivious algorithms for decision tree and support vector machine (SVM) evaluation.
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