Robust and Verifiable MPC with Applications to Linear Machine Learning Inference

• URL: http://arxiv.org/abs/2506.00518v1
• Date: Sat, 31 May 2025 11:26:57 GMT
• Title: Robust and Verifiable MPC with Applications to Linear Machine Learning Inference
• Authors: Tzu-Shen Wang, Jimmy Dani, Juan Garay, Soamar Homsi, Nitesh Saxena,
• Abstract summary: We present an efficient multi-party computation protocol that provides strong security guarantees in settings with dishonest majority of participants.<n>With complete identifiability, honest parties can detect and unanimously agree on the identity of any malicious party.<n>We benchmark our protocol on a ML-as-a-service scenario, wherein clients off-load the desired computation to the servers, and verify the computation result.
• Score: 1.3612043566819643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we present an efficient secure multi-party computation MPC protocol that provides strong security guarantees in settings with dishonest majority of participants who may behave arbitrarily. Unlike the popular MPC implementation known as SPDZ [Crypto '12], which only ensures security with abort, our protocol achieves both complete identifiability and robustness. With complete identifiability, honest parties can detect and unanimously agree on the identity of any malicious party. Robustness allows the protocol to continue with the computation without requiring a restart, even when malicious behavior is detected. Additionally, our approach addresses the performance limitations observed in the protocol by Cunningham et al. [ICITS '17], which, while achieving complete identifiability, is hindered by the costly exponentiation operations required by the choice of commitment scheme. Our protocol is based on the approach by Rivinius et al. [S&P '22], utilizing lattice-based commitment for better efficiency. We achieved robustness with the help of a semi-honest trusted third party. We benchmark our robust protocol, showing the efficient recovery from parties' malicious behavior. Finally, we benchmark our protocol on a ML-as-a-service scenario, wherein clients off-load the desired computation to the servers, and verify the computation result. We benchmark on linear ML inference, running on various datasets. While our efficiency is slightly lower compared to SPDZ's, we offer stronger security properties that provide distinct advantages.

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