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

• Main Authors: ZHAO, Bowen, LI, Yingjiu, LIU, Ximeng, PANG, Hwee Hwa, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Person Re-IDentification; Image privacy; Secure computing; Threshold homomorphism; Computer and Systems Architecture
• Online Access: https://ink.library.smu.edu.sg/sis_research/7597
• Institution: Singapore Management University

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.