Paper 2023/1863

Efficient Secure Multiparty Computation for Multidimensional Arithmetics and Its Application in Privacy-Preserving Biometric Identification

Dongyu Wu, Beijing Institute of Mathematical Sciences and Applications
Bei Liang, Beijing Institute of Mathematical Sciences and Applications
Zijie Lu, Beijing Institute of Mathematical Sciences and Applications
Jintai Ding, Beijing Institute of Mathematical Sciences and Applications
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
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
Creative Commons Attribution-NonCommercial-ShareAlike
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}
}
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