CODER: Protecting Privacy in Image Retrieval With Differential Privacy
Haonan Yan, Xiaoguang Li, Wenjing Zhang, Qian Chen, Bin Wang, Hui Li, Xiaodong Sheldon Lin · IEEE Transactions on Dependable and Secure Computing · 2024
Image retrieval techniques can be easily abused to violate personal privacy with images containing individuals' sensitive information. For example, people's identity information can be inferred from their face photos. Therefore, images should be sanitized before being shared or transmitted. However, previous works on image privacy protection suffer from either no provable privacy protection or poor utility with privacy guarantee. In this work, we proposeCODER, a privacy protection mechanism in image retrieval, with provable privacy guarantee as well as improved utility. In particular,CODERachieves metric differential privacy and adopts a newly proposed distortion metric definition which measures the distance more precisely to improve utility. The novel distortion metric can be applied to an arbitrary k-dimensional metric space with stronger image privacy protection. We theoretically analyze the privacy guarantee and rigorous utility bound ofCODER. We also experimentally compare its performance with two state-of-the-art works on two widely used face datasets. The results show thatCODERsignificantly improves the utility of the protected images and demonstrates its superiority in terms of the privacy-utility trade-off over the compared works. Finally, we perform reliability verification on both discriminative and generative models to demonstrate the practicality ofCODER