Work

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

Year: 2024

Type: article

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.

Source: IEEE Transactions on Visualization and Computer Graphics

DOI: 10.1109/tvcg.2024.3456304

Authors: Xumeng Wang, Shuangcheng Jiao, Chris Bryan

Institutions: Nankai University, Arizona State University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

Awards: G2405826516, SaTC: CORE: Small: Effective Design and Recommendation for Privacy-Preserving Data Visualizations, G5662739716