a privacy-preserving mechanism for fingerprint identification

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

Research output: Contribution to journalArticle


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 $\epsilon$-differential privacy and maintain an efficient fingerprint
Original languageEnglish
JournalACM Transactions on Internet Technology
Publication statusE-pub ahead of print - Mar 2020

Fingerprint Dive into the research topics of 'FinPrivacy: a privacy-preserving mechanism for fingerprint identification'. Together they form a unique fingerprint.

Cite this