Issue |
Security and Safety
Volume 1, 2022
|
|
---|---|---|
Article Number | 2021001 | |
Number of page(s) | 43 | |
Section | Information Network | |
DOI | https://doi.org/10.1051/sands/2021001 | |
Published online | 14 June 2022 |
Review
Concretely efficient secure multi-party computation protocols: survey and more
1
State Key Laboratory of Cryptology, Beijing, 100878, China
2
State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China
* Corresponding author (email: yangk@sklc.org)
Received:
11
October
2021
Revised:
5
November
2021
Accepted:
6
December
2021
Secure multi-party computation (MPC) allows a set of parties to jointly compute a function on their private inputs, and reveals nothing but the output of the function. In the last decade, MPC has rapidly moved from a purely theoretical study to an object of practical interest, with a growing interest in practical applications such as privacy-preserving machine learning (PPML). In this paper, we comprehensively survey existing work on concretely efficient MPC protocols with both semi-honest and malicious security, in both dishonest-majority and honest-majority settings. We focus on considering the notion of security with abort, meaning that corrupted parties could prevent honest parties from receiving output after they receive output. We present high-level ideas of the basic and key approaches for designing different styles of MPC protocols and the crucial building blocks of MPC. For MPC applications, we compare the known PPML protocols built on MPC, and describe the efficiency of private inference and training for the state-of-the-art PPML protocols. Furthermore, we summarize several challenges and open problems to break though the efficiency of MPC protocols as well as some interesting future work that is worth being addressed. This survey aims to provide the recent development and key approaches of MPC to researchers, who are interested in knowing, improving, and applying concretely efficient MPC protocols.
Key words: Secure multi-party computation / Privacy-preserving machine learning / Secret sharings / Garbled circuits / Oblivious transfer and its arithmetic generalization
Citation: Feng D and Yang K. Concretely efficient secure multi-party computation protocols: survey and more. Security and Safety 2022; 1: 2021001. https://doi.org/10.1051/sands/2021001
© The Author(s) 2022. Published by EDP Sciences and China Science Publishing & Media Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.