Clustering-based Efficient Privacy-preserving Face Recognition Scheme without Compromising Accuracy

Author:

Liu Meng1,Hu Hongsheng2,Xiang Haolong2,Yang Chi3,Lyu Lingjuan4,Zhang Xuyun5

Affiliation:

1. Shandong University, Weihai, China

2. The University of Auckland, New Zealand

3. Huazhong University of Science and Technology, Wuhan, China

4. Ant Group, Hangzhou, China

5. Macquarie University, Australia

Abstract

Recently, biometric identification has been extensively used for border control. Some face recognition systems have been designed based on Internet of Things. But the rich personal information contained in face images can cause severe privacy breach and abuse issues during the process of identification if a biometric system has compromised by insiders or external security attacks. Encrypting the query face image is the state-of-the-art solution to protect an individual’s privacy but incurs huge computational cost and poses a big challenge on time-critical identification applications. However, due to their high computational complexity, existing methods fail to handle large-scale biometric repositories where a target face is searched. In this article, we propose an efficient privacy-preserving face recognition scheme based on clustering. Concretely, our approach innovatively matches an encrypted face query against clustered faces in the repository to save computational cost while guaranteeing identification accuracy via a novel multi-matching scheme. To the best of our knowledge, our scheme is the first to reduce the computational complexity from O(M) in existing methods to approximate O (√ M ), where M is the size of a face repository. Extensive experiments on real-world datasets have shown the effectiveness and efficiency of our scheme.

Funder

ARC DECRA

Australian Government

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

Reference46 articles.

1. Charm: A framework for rapidly prototyping cryptosystems;Akinyele Joseph A.;J. Cryptogr. Eng.,2013

2. AT&T Laboratories Cambridge. 2018. The Database of Faces (formerly “The ORL Database of Faces”). Retrieved from http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html. AT&T Laboratories Cambridge. 2018. The Database of Faces (formerly “The ORL Database of Faces”). Retrieved from http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.

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