Paper 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)
- Category
- Cryptographic protocols
- Publication info
- Published elsewhere. Minor revision. Cryptology and Network Security (CANS) 2024
- DOI
- 10.1007/978-981-97-8013-6_1
- Keywords
- tensor tripleMPCBeaver tripleVOLEprivacy-preservingbiometric identificationmachine learning
- Contact author(s)
-
wudongyu @ bimsa cn
lbei @ bimsa cn
luzijie @ bimsa cn
dinglab @ bimsa cn - History
- 2024-10-08: last of 2 revisions
- 2023-12-05: received
- See all versions
- Short URL
- https://ia.cr/2023/1863
- License
-
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}, doi = {10.1007/978-981-97-8013-6_1}, url = {https://eprint.iacr.org/2023/1863} }