Paper 2023/1863
Efficient Secure Multiparty Computation for Multidimensional Arithmetics and Its Application in Privacy-Preserving Biometric Identification
, Beijing Institute of Mathematical Sciences and Applications, Beijing Institute of Mathematical Sciences and Applications, Beijing Institute of Mathematical Sciences and Applications, Beijing Institute of Mathematical Sciences and ApplicationsAbstract
Over years of the development of secure multi-party computation (MPC), many sophisticated functionalities have been made pratical and multi-dimensional operations occur more and more frequently in MPC protocols, especially in protocols involving datasets of vector elements, such as privacy-preserving biometric identification and privacy-preserving machine learning. In this paper, we introduce a new kind of correlation, called tensor triples, which is designed to make multi-dimensional MPC protocols more efficient. We will discuss the generation process, the usage, as well as the applications of tensor triples and show that it can accelerate privacy-preserving biometric identification protocols, such as FingerCode, Eigenfaces and FaceNet, by more than 1000 times, with reasonable offline costs.
Metadata
- Available format(s)
-
PDF
- Category
- Cryptographic protocols
- Publication info
- Preprint.
- Keywords
- tensor tripleMPCBeaver tripleVOLEprivacy-preservingbiometric identificationmachine learning
- Contact author(s)
-
wudongyu @ bimsa cn
lbei @ bimsa cn
luzijie @ bimsa cn
dinglab @ bimsa cn - History
- 2024-04-17: revised
- 2023-12-05: received
- See all versions
- Short URL
- https://ia.cr/2023/1863
- License
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CC BY-NC-SA
BibTeX
@misc{cryptoeprint:2023/1863,
author = {Dongyu Wu and Bei Liang and Zijie Lu and Jintai Ding},
title = {Efficient Secure Multiparty Computation for Multidimensional Arithmetics and Its Application in Privacy-Preserving Biometric Identification},
howpublished = {Cryptology ePrint Archive, Paper 2023/1863},
year = {2023},
note = {\url{https://eprint.iacr.org/2023/1863}},
url = {https://eprint.iacr.org/2023/1863}
}
