Secure and verifiable iris authentication system using fully homomorphic encryption. (January 2021)

• Record Type: Journal Article
• Authors: Morampudi, Mahesh Kumar; Prasad, Munaga V.N.K.; Verma, Mridula; Raju, U.S.N.
• Is Part Of: Computers & electrical engineering. Volume 89(2021)
• Publication Date: 2021-01
• Subjects: Biometrics -- Privacy-preserving -- Homomorphic encryption -- Multi-class Perceptron -- Nearest Neighbor; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106924
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: With the escalated usage of a biometric authentication system (BAS), template protection for biometrics attracted research interest in recent years. The assumption behind the existing homomorphic encryption-based BASs is that the server performs the computations honestly. In a malicious server setting, the server may return an arbitrary result to save the computational resources, which may result in false accept/reject. To tackle this challenge, we propose a secure and verifiable classification based iris authentication system (SvaS). SvaS aims to achieve both privacy-preserving (PP) training and PP classification of Nearest Neighbor and Multi-class Perceptron models. The Fan-vercauteren scheme provides confidentiality for the iris templates, and aggregate verification vector helps to verify the correctness of the computed classification result. Extensive experimental results on benchmark iris databases demonstrate that SvaS provides privacy to the iris templates with no loss in accuracy and eliminates the need to trust the server. Graphical abstract: Highlights: Reliable authentication even in a malicious environment. Implemented the nearest neighbor classifier on encrypted data. Implemented the multi-class perceptron on encrypted data. Includes a procedure to check the classification result computed by the cloud server.