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

arXiv: https://arxiv.org/abs/2208.13418 · HTML: https://mlsanity.com/paper/2208.13418/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2022.

## Built on by (most cited first, 4 of 4)

- [PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation](https://mlsanity.com/paper/2307.09036/) (2023)
- [Towards an Understanding and Explanation for Mixed-Initiative Artificial Scientific Text Detection](https://mlsanity.com/paper/2304.05011/) (2023)
- [VideoPro: A Visual Analytics Approach for Interactive Video Programming](https://mlsanity.com/paper/2308.00401/) (2023)
- [Semantic Draw Engineering for Text-to-Image Creation](https://mlsanity.com/paper/2401.04116/) (2023)

## Abstract (arXiv)

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.