Multi-Party Replicated Secret Sharing over a Ring with Applications to Privacy-Preserving Machine Learning
Authors: Alessandro Baccarini (University at Buffalo (SUNY)), Marina Blanton (University at Buffalo (SUNY)), Chen Yuan (University at Buffalo (SUNY))
Volume: 2023
Issue: 1
Pages: 608–626
DOI: https://doi.org/10.56553/popets-2023-0035
Abstract: 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.
Keywords: secure multi-party computation, secret sharing, privacy-preserving machine learning
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