Secure Hamming distance computation for biometrics using ideal-lattice and ring-LWE homomorphic encryption

Masaya Yasuda

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

With widespread development of biometrics, concerns about security and privacy are rapidly increasing. Homomorphic encryption enables us to operate on encrypted data without decryption, and it can be applied to construct a privacy-preserving biometric system. In this article, we apply two homomorphic encryption schemes based on ideal-lattice and ring-LWE (Learning with Errors), which both have homomorphic correctness over the ring of integers of a cyclotomic field. We compare the two schemes in applying them to privacy-preserving biometrics. In biometrics, the Hamming distance is used as a metric to compare two biometric feature vectors for authentication. We propose an efficient method for secure Hamming distance. Our method can pack a biometric feature vector into a single ciphertext, and it enables efficient computation of secure Hamming distance over our packed ciphertexts.

Original languageEnglish
Pages (from-to)85-103
Number of pages19
JournalInformation Security Journal
Volume26
Issue number2
DOIs
Publication statusPublished - Mar 4 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Information Systems and Management

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