A Comparative Evaluation of Differentially Private Image Obfuscation

Dominick Reilly, Liyue Fan

Abstract: Image data may contain sensitive information, such as face and iris, which can be misused if in the hands of an adversary. As image data is continuously being collected and shared, it is imperative to ensure the privacy of image data. Widely used image obfuscation methods apply blurring or pixelization to those sensitive regions. However, they are prone to inference attacks, and do not provide quantifiable privacy guarantees. Recently, several obfuscation approaches have been proposed to satisfy the rigorous notion of differential privacy. The goal of this work is to provide a comparative evaluation of those previously proposed approaches in the context of obfuscating face and iris images. We synthesize existing differentially private obfuscation methods and analyze their privacy guarantees. Furthermore, we conduct an extensive empirical evaluation regarding practical utility and privacy protection, with real-world face and iris image datasets. We find that DP-SVD outperforms other methods on several privacy and utility measures. Moreover, we provide an in-depth discussion of our results and point to several considerations when applying those differentially private image obfuscation methods.