Paper 2023/1863 Original: eprint.iacr.org/2023/1863 ↗
Efficient Secure Multiparty Computation for Multidimensional Arithmetics and Its Application in Privacy-Preserving Biometric Identification
Abstract
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.
- History
- 2024-10-08: last updated
- 2023-12-05: published
- Short URL
- https://ia.cr/2023/1863
- License
- https://creativecommons.org/licenses/by-nc-sa/4.0/
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},
url = {https://eprint.iacr.org/2023/1863}
}