FREED: an efficient privacy-preserving solution for person re-identification

Publication Type

Conference Proceeding Article

Publication Date

6-2022

Abstract

Person Re-IDentification (Re-ID) is a critical technology to identify a target person from captured person images by surveillance cameras. However, person Re-ID has triggered great concerns of personal image privacy. Although the law (e.g., GDPR) has stipulated person images are personal private data, there is no an efficient solution to tackle the image privacy concern for person Re-ID. To this end, we propose FREED, the first system solution for privacy-preserving person Re-ID, which supports the state-of-the-art person Re-ID operations on encrypted feature vectors of person images. To handle the encryption of feature vectors effectively and enable person Re-ID operations on encrypted feature vectors efficiently, FREED develops a suite of batch secure computing protocols based on a twin-server architecture and the threshold Paillier cryptosystem. We demonstrate our secure computing protocols are more efficient than existing protocols and FREED achieves a precision equal to the state-of-the-art plaintext method.

Keywords

Person Re-IDentification, Image privacy, Secure computing, Threshold homomorphism

Discipline

Computer and Systems Architecture

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 5th IEEE Conference on Dependable and Secure Computing, Edinburgh, United Kingdom, 2022 June 22 - 24

ISBN

9781665421423

Identifier

10.1109/DSC54232.2022.9888863

City or Country

Edinburgh

Additional URL

https://doi.org/10.1109/DSC54232.2022.9888863

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