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Communication-Efficient (Client-Aided) Secure Two-Party Protocols and Its Application

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arxiv 1907.03415 v2 pith:LUFLZDRS submitted 2019-07-08 cs.CR

classification cs.CR
keywords communicationprotocolsnumberroundssecurebitscommunication-efficientcomparison
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Secure multi-party computation (MPC) allows a set of parties to compute a function jointly while keeping their inputs private. Compared with the MPC based on garbled circuits,some recent research results show that MPC based on secret sharing (SS) works at a very high speed. Moreover, SS-based MPC can be easily vectorized and achieve higher throughput. In SS-based MPC, however, we need many communication rounds for computing concrete protocols like equality check, less-than comparison, etc. This property is not suited for large-latency environments like the Internet (or WAN). In this paper, we construct semi-honest secure communication-efficient two-party protocols. The core technique is Beaver triple extension, which is a new tool for treating multi-fan-in gates, and we also show how to use it efficiently. We mainly focus on reducing the number of communication rounds, and our protocols also succeed in reducing the number of communication bits (in most cases). As an example, we propose a less-than comparison protocol (under practical parameters) with three communication rounds. Moreover, the number of communication bits is also $38.4\%$ fewer. As a result, total online execution time is $56.1\%$ shorter than the previous work adopting the same settings. Although the computation costs of our protocols are more expensive than those of previous work, we confirm via experiments that such a disadvantage has small effects on the whole online performance in the typical WAN environments.

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  1. Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries

    cs.LG 2026-07 reject novelty 5.0 of 10

    A secure aggregation protocol for sign-based federated learning computes the majority vote in one round with linear offline cost, but the claimed degree-halving simplification breaks at zero inputs and for inverse terms.

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