SNUSE: A secure computation approach for large-scale user re-enrollment in biometric authentication systems

Abstract

Abstract Recent years have witnessed an increasing demand for biometrics based identification, authentication and access control (BIA) systems, which offer convenience, ease of use, and (in some cases) improved security. In contrast to other methods, such as passwords or pins, BIA systems face new unique challenges; chiefly among them is ensuring long-term confidentiality of biometric data stored in backends, as such data has to be secured for the lifetime of an individual. Cryptographic approaches such as Fuzzy Extractors (FE) and Fuzzy Vaults (FV) have been developed to address this challenge. FE/FV do not require storing any biometric data in backends, and instead generate and store helper data that enables BIA when a new biometric reading is supplied. Security of FE/FV ensures that an adversary obtaining such helper data cannot (efficiently) learn the biometric. Relying on such cryptographic approaches raises the following question: what happens when helper data is lost or destroyed (e.g., due to a failure, or malicious activity), or when new helper data has to be generated (e.g., in response to a breach or to update the system)? Requiring a large number of users to physically re-enroll is impractical, and the literature falls short of addressing this problem. In this paper we develop SNUSE , a secure computation based approach for non-interactive re-enrollment of a large number of users in BIA systems. We prototype SNUSE to illustrate its feasibility, and evaluate its performance and accuracy on two biometric modalities, fingerprints and iris scans. Our results show that thousands of users can be securely re-enrolled in seconds without affecting the accuracy of the system.