CODER: Protecting Privacy in Image Retrieval With Differential Privacy

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 propose
CODER
, a privacy protection mechanism in image retrieval, with provable privacy guarantee as well as improved utility. In particular,
CODER
achieves 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 of
CODER
. We also experimentally compare its performance with two state-of-the-art works on two widely used face datasets. The results show that
CODER
significantly 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 of
CODER
.