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
Citation
ZHAO, Bowen; LI, Yingjiu; LIU, Ximeng; PANG, Hwee Hwa; and DENG, Robert H..
FREED: an efficient privacy-preserving solution for person re-identification. (2022). Proceedings of the 5th IEEE Conference on Dependable and Secure Computing, Edinburgh, United Kingdom, 2022 June 22 - 24.
Available at: https://ink.library.smu.edu.sg/sis_research/7597
Additional URL
https://doi.org/10.1109/DSC54232.2022.9888863