Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Privacy-Preserving Fully Homomorphic Encryption and Parallel Computation Based Biometric Data Matching

Version 1 : Received: 26 July 2020 / Approved: 27 July 2020 / Online: 27 July 2020 (06:19:29 CEST)

How to cite: Catak, F.O.; Yildirim Yayilgan, S.; Abomhara, M. A Privacy-Preserving Fully Homomorphic Encryption and Parallel Computation Based Biometric Data Matching. Preprints 2020, 2020070658 (doi: 10.20944/preprints202007.0658.v1). Catak, F.O.; Yildirim Yayilgan, S.; Abomhara, M. A Privacy-Preserving Fully Homomorphic Encryption and Parallel Computation Based Biometric Data Matching. Preprints 2020, 2020070658 (doi: 10.20944/preprints202007.0658.v1).

Abstract

One of the most reliable methods of authentication used today is biometric matching. This authentication process, which is done by using biometrics information such as fingerprint, iris, face, etc. is used in many application areas. Authentication at border gates is one of these areas. However, some restrictions have been introduced to storing and using such data, especially with the General Data Protection Regulation (GDPR). The main goal of this work is to find the practical implementation of fully homomorphic encryption-based biometric matching in border controls. In this paper, we propose a biometric authentication system based on hash expansion and fully homomorphic encryption features, considering these restrictions. One of the most significant drawbacks of the homomorphic encryption method is the long execution time. We solved this problem by executing the matching algorithm in parallel manner. The proposed scheme is implemented as proof-of-concept in the SMILE, and its advantages in privacy preservation has been demonstrated.

Subject Areas

biometric matching; fully homomorphic encryption; privacy-preserving techniques

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