Communication-Efficient (Client-Aided) Secure Two-Party Protocols and Its Application
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2019.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
4 papers.
- 2021Tetrad: Actively Secure 4PC for Secure Training and Inferencecited by 8
- 2021MPCLeague: Robust MPC Platform for Privacy-Preserving Machine Learningcited by 3
- 2022MPClan: Protocol Suite for Privacy-Conscious Computationscited by 1
- 2025PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networkscited by 0
- 2025PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks4 Nov 2025
- 2022MPClan: Protocol Suite for Privacy-Conscious Computations24 Jun 2022
- 2021MPCLeague: Robust MPC Platform for Privacy-Preserving Machine Learning26 Dec 2021
- 2021Tetrad: Actively Secure 4PC for Secure Training and Inference5 Jun 2021