DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy

TL;DR

DPVisCreator is a system that enables privacy-preserving data visualizations by incorporating user-specified pattern constraints into a differential privacy model, balancing data utility and privacy.

Contribution

The paper introduces pattern constraints into a Bayesian network-based differential privacy model to preserve data patterns in visualizations, which was not addressed in prior work.

Findings

Effective preservation of data patterns demonstrated through quantitative evaluation.

Successful case studies show practical utility of the approach.

Expert interviews confirm usability and relevance of the system.

Abstract

Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facilitates data custodians to generate multiple private charts while maintaining user-preferred patterns. To this end, we introduce pattern constraints to model users' preferences over data patterns in the dataset and incorporate them into the proposed Bayesian network-based Differential Privacy (DP) model PriVis. A prototype system, DPVisCreator, is developed to assist data custodians in implementing our approach. The effectiveness of our approach is demonstrated with quantitative evaluation of pattern utility under the different levels of privacy protection, case studies, and semi-structured expert interviews.