Lattice-Based Secure Biometric Authentication for Hamming Distance

Jung Hee Cheon, Dongwoo Kim, Duhyeong Kim, Joohee Lee, Junbum Shin, Yongsoo Song

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Biometric authentication is a protocol which verifies a user’s authority by comparing her biometric with the pre-enrolled biometric template stored in the server. Biometric authentication is convenient and reliable; however, it also brings privacy issues since biometric information is irrevocable when exposed. In this paper, we propose a new user-centric secure biometric authentication protocol for Hamming distance. The biometric data is always encrypted so that the verification server learns nothing about biometric information beyond the Hamming distance between enrolled and queried templates. To achieve this, we construct a single-key function-hiding inner product functional encryption for binary strings whose security is based on a variant of the Learning with Errors problem. Our protocol consists of a single round, and is almost optimal in the sense that its time and space complexity grow quasi-linearly with the size of biometric templates. On implementation with concrete parameters, for binary strings of size ranging from 579 to 18,229 bytes (according to NIST IREX IX report), our scheme outperforms previous work from the literature.

Original languageEnglish
Title of host publicationInformation Security and Privacy - 26th Australasian Conference, ACISP 2021, Proceedings
EditorsJoonsang Baek, Sushmita Ruj
PublisherSpringer Science and Business Media Deutschland GmbH
Pages653-672
Number of pages20
ISBN (Print)9783030905668
DOIs
StatePublished - 2021
Event26th Australasian Conference on Information Security and Privacy, ACISP 2021 - Virtual, Online
Duration: 1 Dec 20213 Dec 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13083 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th Australasian Conference on Information Security and Privacy, ACISP 2021
CityVirtual, Online
Period1/12/213/12/21

Keywords

  • Biometric authentication
  • Inner product functional encryption
  • Learning with errors

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