Interpreting Disparate Privacy-Utility Tradeoff in Adversarial Learning via Attribute Correlation

Likun Zhang, Yahong Chen, Ang Li, Binghui Wang, Yiran Chen, Fenghua Li, Jin Cao, Ben Niu; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 4701-4709

Adversarial learning is commonly used to extract latent data representations which are expressive to predict the target attribute but indistinguishable in the privacy attribute. However, whether they can achieve an expected privacy-utility tradeoff is of great uncertainty. In this paper, we posit it is the complex interaction between different attributes in the training set that causes disparate tradeoff results. We first formulate the measurement of utility, privacy and their tradeoff in adversarial learning. Then we propose the metrics of Statistical Reliability (SR) and Feature Reliability (FR) to quantify the relationship between attributes. Specifically, SR reflects the co-occurrence sampling bias of the joint distribution between two attributes. Beyond the explicit dependence, FR exploits the intrinsic interaction one attribute exerts on the other via exploring the representation disentanglement. We validate the metrics in an adversarial learning scheme on CelebA and LFW dataset with a suite of target-privacy attribute pairs. Experiments demonstrate the strong correlations between the metrics and utility, privacy and their tradeoff. We further conclude how to use SR and FR as a guide to the selection of the privacy-utility tradeoff parameter.