Paper 2024/654

Monchi: Multi-scheme Optimization For Collaborative Homomorphic Identification

Alberto Ibarrondo, Copper.co
Ismet Kerenciler, Télécom ParisTech, EURECOM
Hervé Chabanne, Télécom ParisTech, Idemia
Vincent Despiegel, Idemia
Melek Önen, EURECOM
Abstract

This paper introduces a novel protocol for privacy-preserving biometric identification, named Monchi, that combines the use of homomorphic encryption for the computation of the identification score with function secret sharing to obliviously compare this score with a given threshold and finally output the binary result. Given the cost of homomorphic encryption, BFV in this solution, we study and evaluate the integration of two packing solutions that enable the regrouping of multiple templates in one ciphertext to improve efficiency meaningfully. We propose an end-to-end protocol, prove it secure and implement it. Our experimental results attest to Monchi's applicability to the real-life use case of an airplane boarding scenario with 1000 passengers,taking less than one second to authorize/deny access to the plane to each passenger via biometric identification while maintaining the privacy of all passengers.

Metadata
Available format(s)
PDF
Category
Cryptographic protocols
Publication info
Published elsewhere. IHMMSec24
DOI
10.1145/3658664.3659633
Keywords
Multiparty Homomorphic EncryptionFunctio Secret SharingSecure Two Party ComputationMasking
Contact author(s)
ibarrond @ eurecom fr
ismet kerenciler @ telecom-paris fr
herve chabanne @ telecom-paris fr
vincent despiegel @ idemia com
melek onen @ eurecom fr
History
2024-04-29: approved
2024-04-29: received
See all versions
Short URL
https://ia.cr/2024/654
License
Creative Commons Attribution-NonCommercial-ShareAlike
CC BY-NC-SA

BibTeX

@misc{cryptoeprint:2024/654,
      author = {Alberto Ibarrondo and Ismet Kerenciler and Hervé Chabanne and Vincent Despiegel and Melek Önen},
      title = {Monchi: Multi-scheme Optimization For Collaborative Homomorphic Identification},
      howpublished = {Cryptology {ePrint} Archive, Paper 2024/654},
      year = {2024},
      doi = {10.1145/3658664.3659633},
      url = {https://eprint.iacr.org/2024/654}
}
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