Paper 2019/1103

Multisketches: Practical Secure Sketches Using Off-the-Shelf Biometric Matching Algorithms

Rahul Chatterjee, M. Sadegh Riazi, Tanmoy Chowdhury, Emanuela Marasco, Farinaz Koushanfar, and Ari Juels

Abstract

Biometric authentication is increasingly being used for large scale human authentication and identification, creating the risk of leaking the biometric secrets of millions of users in the case of database compromise. Powerful ``fuzzy'' cryptographic techniques for biometric template protection, such as secure sketches, could help in principle, but go unused in practice. This is because they would require new biometric matching algorithms with potentially much-diminished accuracy. We introduce a new primitive called a multisketch that generalizes secure sketches. Multisketches can work with existing biometric matching algorithms to generate strong cryptographic keys from biometric data reliably. A multisketch works on a biometric database containing multiple biometrics --- e.g., multiple fingerprints --- of a moderately large population of users (say, thousands). It conceals the correspondence between users and their biometric templates, preventing an attacker from learning the biometric data of a user in the advent of a breach, but enabling derivation of user-specific secret keys upon successful user authentication. We design a multisketch over tenprints --- fingerprints of ten fingers --- called TenSketch. We report on a prototype implementation of TenSketch, showing its feasibility in practice. We explore several possible attacks against TenSketch database and show, via simulations with real tenprint datasets, that an attacker must perform a large amount of computation to learn any meaningful information from a stolen TenSketch database.