research-article

Grote: Group Testing for Privacy-Preserving Face Identification

Authors:

Alberto Ibarrondo,

Hervé Chabanne,

Vincent Despiegel,

Melek ÖnenAuthors Info & Claims

CODASPY '23: Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy

Pages 117 - 128

https://doi.org/10.1145/3577923.3583656

Published: 24 April 2023 Publication History

Abstract

This paper proposes a novel method to perform privacy-preserving face identification based on the notion of group testing, and applies it to a solution using the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme. Securely computing the closest reference template to a given live template requires K comparisons, as many as there are identities in a biometric database. Our solution, named Grote, replaces element-wise testing by group testing to drastically reduce the number of such costly, non-linear operations in the encrypted domain from K to up to 2\sqrtK . More specifically, we approximate the max of the coordinates of a large vector by raising to the α-th power and cumulative sum in a 2D layout, incurring a small impact in the accuracy of the system while greatly speeding up its execution. We implement Grote and evaluate its performance.

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Cited By

Choi HKim JSong CWoo SKim HSerra ESpezzano F(2024)Blind-Match: Efficient Homomorphic Encryption-Based 1:N Matching for Privacy-Preserving Biometric IdentificationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680017(4423-4430)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680017
Nikpey HSarkar SBidokhti S(2024)Group Testing with General Correlation Using Hypergraphs2024 IEEE International Symposium on Information Theory (ISIT)10.1109/ISIT57864.2024.10619244(3225-3230)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ISIT57864.2024.10619244
Mi YZhong ZHuang YJi JXu JWang JWang SDing SZhou S(2024)Privacy-Preserving Face Recognition Using Trainable Feature Subtraction2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00036(297-307)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00036
Show More Cited By

Index Terms

Grote: Group Testing for Privacy-Preserving Face Identification
1. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics
    2. Privacy-preserving protocols

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cover image ACM Conferences

CODASPY '23: Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy

April 2023

304 pages

ISBN:9798400700675

DOI:10.1145/3577923

General Chair:
Mohamed Shehab
University of North Carolina at Charlotte, USA
,
Program Chairs:
Maribel Fernandez
King's College London, UK
,
Ninghui Li
Purdue University, USA

Copyright © 2023 ACM.

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Published: 24 April 2023

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CODASPY '23: Thirteenth ACM Conference on Data and Application Security and Privacy

April 24 - 26, 2023

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Cited By

Choi HKim JSong CWoo SKim HSerra ESpezzano F(2024)Blind-Match: Efficient Homomorphic Encryption-Based 1:N Matching for Privacy-Preserving Biometric IdentificationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680017(4423-4430)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680017
Nikpey HSarkar SBidokhti S(2024)Group Testing with General Correlation Using Hypergraphs2024 IEEE International Symposium on Information Theory (ISIT)10.1109/ISIT57864.2024.10619244(3225-3230)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ISIT57864.2024.10619244
Mi YZhong ZHuang YJi JXu JWang JWang SDing SZhou S(2024)Privacy-Preserving Face Recognition Using Trainable Feature Subtraction2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00036(297-307)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00036
Grivet Sébert AZuber MStan OSirdey RGouy-Pailler CBrenner MCostache ARohloff K(2023)A Probabilistic Design for Practical Homomorphic Majority Voting with Intrinsic Differential PrivacyProceedings of the 11th Workshop on Encrypted Computing & Applied Homomorphic Cryptography10.1145/3605759.3625258(47-58)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3605759.3625258
Bassit AHahn FRezgui ZKelly UVeldhuis RPeter A(2023)Template Recovery Attack on Homomorphically Encrypted Biometric Recognition Systems with Unprotected Threshold Comparison2023 IEEE International Joint Conference on Biometrics (IJCB)10.1109/IJCB57857.2023.10449211(1-10)Online publication date: 25-Sep-2023
https://doi.org/10.1109/IJCB57857.2023.10449211

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