Efficient and privacy-preserving online face recognition over encrypted outsourced data

Publication Type

Conference Proceeding Article

Publication Date

9-2018

Abstract

With the development of image processing technology and the pervasiveness of mobile devices, face recognition, which can be used to offer convenient and efficient individual authentication service, has attracted considerable interest in recent years. However, people's concern about their face data being leaked during the face recognition process impedes the flourish of face recognition. To address this problem, we present a novel privacy-preserving online face recognition scheme over encrypted outsourced data, named EPFR. With EPFR, a user can achieve secure, accurate and efficient authentication service without disclosing her/his face data. Specifically, an improved homomorphic encryption technology is introduced to provide an efficient online face recognition service based on the Eigenface algorithm. Through extensive analysis, we show that users' face data are kept confidential during the online face recognition process. In addition, we implement the scheme with a real face database, and simulation results demonstrate that the scheme can be used to provide efficient and accurate online face recognition service.

Keywords

Face recognition, Online authentication, Outsource, Privacy-preserving

Discipline

Software Engineering

Publication

Proceedings of the 18th IEEE International Conference on Computer and Information Technology, Halifax, Canada, 2018 July 30 - August 3

ISBN

9781538679753

Identifier

10.1109/Cybermatics_2018.2018.00089

City or Country

Halifax, Canada

Additional URL

https://doi.org/10.1109/Cybermatics_2018.2018.00089

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