  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.
