FinPrivacy: a privacy-preserving mechanism for fingerprint identification

Tao Wang, Zhigao Zheng, Ali Kashif Bashir, Alireza Jolfaei, Yanyan Xu

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Fingerprint provides an extremely convenient way of identification for a wide range of real-life applications owing to its universality, uniqueness, collectability, and invariance. However, digitized fingerprints may reveal the privacy of individuals. Differential privacy is a promising privacy-preserving solution that is enforced by injecting random noise into preserved objects, such that an adversary with arbitrary background knowledge cannot infer private input from the noisy results. This study proposes FinPrivacy, a privacy-preserving mechanism for fingerprint identification. This mechanism utilizes the low-rank matrix approximation to reduce the dimensionality of fingerprint and the exponential mechanism to carefully determine the value of the optimal rank. Thereafter, FinPrivacy injects Laplace noise to the singular values of the approximated singular matrix, thereby trading off between privacy and utility. Analytic proofs and results of the comparative experiments demonstrate that FinPrivacy can simultaneously enforce ɛ-differential privacy and maintain an efficient fingerprint recognition.
Original languageEnglish
Article number56
Pages (from-to)1-15
Number of pages15
JournalACM Transactions on Internet Technology
Issue number3
Early online dateMar 2020
Publication statusPublished - Aug 2021


  • fingerprint
  • differential privacy
  • low-rank matrix approximation
  • sensitivity


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