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Title:
Arithmetic Tuples for MPC
Authors: Pascal Reisert, Marc Rivinius, Toomas Krips, and Ralf Kuesters
Abstract:Some of the most efficient protocols for Multi-Party Computation (MPC) use a two-phase approach where correlated randomness, in particular Beaver triples, is generated in the offline phase and then used to speed up the online phase. Recently, more complex correlations have been introduced to optimize certain operations even further, such as matrix triples for matrix multiplications. In this paper, our goal is to speed up the evaluation of multivariate polynomials and therewith of whole arithmetic circuits in the online phase. To this end, we introduce a new form of correlated randomness: arithmetic tuples. Arithmetic tuples can be fine tuned in various ways to the constraints of application at hand, in terms of round complexity, bandwidth, and tuple size. We show that for many real-world setups an arithmetic tuples based online phase outperforms state-of-the-art protocols based on Beaver triples.
ePrint: https://eprint.iacr.org/2022/667
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