New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning

By Ivan Bjerre Damgård, Daniel E. Escudero, Tore Kasper Frederiksen, Marcel Keller, Peter Scholl, Nikolaj Volgushev. 2019

182 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

Secure Evaluation of Quantized Neural Networks (2019, methodology): “This can be done in the dishonest majority setting as proposed in [17], or more generally, we can let each party Pi propose a bit 〈bi〉 (which can be checked to be a bit indeed by verifying that 〈bi〉 · (1− 〈bi〉) is 0) and then the parties XOR these bits together to get one single random bit.”
ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation (2020, background): “Moreover, the benchmarking results of [94] and the works of [17, 34, 35, 37, 39] have showcased the efficiency improvements of protocols compared to rings over their field counterparts.”
CrypTen: Secure Multi-Party Computation Meets Machine Learning (2021, methodology): “To facilitate secure computations, CRYPTEN implements arithmetic secret sharing [22, 23] and binary secret sharing [32], as well as conversions between these two types of sharing [24].”
MP-SPDZ: A Versatile Framework for Multi-Party Computation (2020, methodology): “[27] later extended this to computation modulo a power of two.”