Grote: Group testing for privacy-preserving face identification

Ibarrondo, Alberto; Chabanne, Hervé; Despiegel, Vincent; Önen, Melek
CODASPY 2023, 13th ACM Conference on Data and Application Security and Privacy, 24-26 April 2023, Charlotte, NC, USA

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 ?? 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 ?? to up to 2√ ??. More specifically, we approximate the max of the coordinates of a large vector by raising to the ??-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.

DOI
HAL
Type:
Conférence
City:
Charlotte
Date:
2023-04-24
Department:
Sécurité numérique
Eurecom Ref:
7213
Copyright:
© ACM, 2023. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in CODASPY 2023, 13th ACM Conference on Data and Application Security and Privacy, 24-26 April 2023, Charlotte, NC, USA https://doi.org/10.1145/3577923.3583656

PERMALINK : https://www.eurecom.fr/publication/7213