Fast and Accurate Likelihood Ratio Based Biometric Verification Secure Against Malicious Adversaries

Amina Bassit*, Florian Hahn, Joep Peeters, Tom Kevenaar, Raymond Veldhuis, Andreas Peter

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Biometric verification has been widely deployed in current authentication solutions as it proves the physical presence of individuals. Several solutions have been developed to protect the sensitive biometric data in such systems that provide security against honest-but-curious (a.k.a. semi-honest) attackers. However, in practice, attackers typically do not act honestly and multiple studies have shown severe biometric information leakage in such honest-but-curious solutions when considering dishonest, malicious attackers. In this paper, we propose a provably secure biometric verification protocol to withstand malicious attackers and prevent biometric data from any leakage. The proposed protocol is based on a homomorphically encrypted log likelihood-ratio (HELR) classifier that supports any biometric modality (e.g., face, fingerprint, dynamic signature, etc.) encoded as a fixed-length real-valued feature vector. The HELR classifier performs an accurate and fast biometric recognition. Furthermore, our protocol, which is secure against malicious adversaries, is designed from a protocol secure against semi-honest adversaries enhanced by zero-knowledge proofs. We evaluate both protocols for various security levels and record a sub-second speed (between 0.37s and 0.88s) for the protocol secure against semi-honest adversaries and between 0.95s and 2.50s for the protocol secure against malicious adversaries.

Original languageEnglish
Pages (from-to)5045-5060
Number of pages16
JournalIEEE transactions on information forensics and security
Volume16
DOIs
Publication statusPublished - 26 Oct 2021

Keywords

  • Authentication
  • Biometric verification
  • Biometrics (access control)
  • Feature extraction
  • Probes
  • Protocols
  • secure two-party computation
  • Security
  • semi-honest and malicious models
  • Servers
  • threshold homomorphic encryption

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