Identifiable, but not visible: A privacy-preserving person reidentfication scheme

Publication Type

Journal Article

Publication Date

4-2023

Abstract

Person re-identification (Person Re-ID) is widely regarded as a promising technique to identify a target person through surveillance cameras in the wild. Nevertheless, person Re-ID leads to severe personal image privacy concerns as personal images are stipulated by laws and guidelines as private data. To address these concerns, this article explores the first solution for building a privacy-preserving person Re-ID system. Specifically, this article formulizes privacy-preserving person Re-ID as similarity metrics of encrypted feature vectors because the underlying operation of person Re-ID is to compute the similarity of feature vectors that are extracted from person images by a machine learning model. However, feature vectors are generally denoted by floating-point numbers. To this end, this article exploits a series of new encoding mechanisms and secure batch computing protocols to encrypt floating-point feature vectors and achieve the underlying operation of person Re-ID. Rigorous theoretical analyses demonstrate that this work achieves person Re-ID without compromising any personal image privacy. Furthermore, the proposed secure batch protocols significantly enhance the performance of privacy-preserving person Re-ID while outputting the same precision as the previous method.

Keywords

Batch computation, image privacy, person Re-ID, privacy-preserving identification, secure computing

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Reliability

First Page

1

Last Page

13

ISSN

0018-9529

Identifier

10.1109/TR.2023.3258983

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TR.2023.3258983

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