$α$-Mutual Information: A Tunable Privacy Measure for Privacy Protection in Data Sharing
It addresses privacy protection in data sharing for applications like images and time-series data, but appears incremental as it builds on existing mutual information concepts with tuning.
This paper tackles the problem of preventing private data disclosure in data sharing by adopting α-Mutual Information as a tunable privacy measure, demonstrating that it yields superior models that effectively thwart attackers and show improved resiliency against side information compared to state-of-the-art methods.
This paper adopts Arimoto's $α$-Mutual Information as a tunable privacy measure, in a privacy-preserving data release setting that aims to prevent disclosing private data to adversaries. By fine-tuning the privacy metric, we demonstrate that our approach yields superior models that effectively thwart attackers across various performance dimensions. We formulate a general distortion-based mechanism that manipulates the original data to offer privacy protection. The distortion metrics are determined according to the data structure of a specific experiment. We confront the problem expressed in the formulation by employing a general adversarial deep learning framework that consists of a releaser and an adversary, trained with opposite goals. This study conducts empirical experiments on images and time-series data to verify the functionality of $α$-Mutual Information. We evaluate the privacy-utility trade-off of customized models and compare them to mutual information as the baseline measure. Finally, we analyze the consequence of an attacker's access to side information about private data and witness that adapting the privacy measure results in a more refined model than the state-of-the-art in terms of resiliency against side information.