Defogger: A Visual Analysis Approach for Data Exploration of Sensitive Data Protected by Differential Privacy

TL;DR

This paper introduces a visual analysis system leveraging reinforcement learning to assist data exploration under differential privacy constraints, addressing privacy-induced uncertainty and limited exploration flexibility.

Contribution

It presents a novel visual analysis approach with reinforcement learning for strategy formulation in privacy-preserving data exploration, supported by a prototype system.

Findings

The approach effectively suggests exploration strategies aligned with user intent.

User studies show improved strategy development for privacy-aware data analysis.

Case studies demonstrate practical applicability in real-world scenarios.

Abstract

Differential privacy ensures the security of individual privacy but poses challenges to data exploration processes because the limited privacy budget incapacitates the flexibility of exploration and the noisy feedback of data requests leads to confusing uncertainty. In this study, we take the lead in describing corresponding exploration scenarios, including underlying requirements and available exploration strategies. To facilitate practical applications, we propose a visual analysis approach to the formulation of exploration strategies. Our approach applies a reinforcement learning model to provide diverse suggestions for exploration strategies according to the exploration intent of users. A novel visual design for representing uncertainty in correlation patterns is integrated into our prototype system to support the proposed approach. Finally, we implemented a user study and two case studies. The results of these studies verified that our approach can help develop strategies that satisfy the exploration intent of users.