SaTC: CORE: Small: Effective Design and Recommendation for Privacy-Preserving Data Visualizations

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This project will develop novel techniques and software tools to support the creation of visualizations that preserve the privacy of sensitive information in the underlying dataset. Visualization is an important technique for communicating information in our society. Unfortunately, visualization can stand at odds with the concept of data privacy, where items in a dataset cannot be fully made public due to the existence of sensitive information that they might contain. Even when the underlying raw data is not published, public-facing visualizations must be carefully designed so that they do not allow the disclosure or inference of sensitive information in the data records. The goal of this project is to make it easy for people to create well-designed, public-facing, privacy-preserving visualizations from datasets that contain sensitive data. The project team will develop mechanisms and software that optimize the design of visualizations (based on established theory and principles) while also considering the privacy risks that might exist in the data. New techniques and tools will be released for public use, as standalone open source projects and/or integrated into popular toolkits and libraries.To accomplish this goal, the project team will develop a visualization grammar, a mechanism for specifying visualization designs without requiring low-level programming or data pre-processing, that supports privacy constraints and sanitization actions. The grammar will be used to establish theories and guidelines for effective visualization design in the context of privacy risks, via conducting large-scale, crowdsourced experiments that assess how different design choices, privacy rules, and sanitization techniques actions affect utility and privacy of the resulting visualizations. The impact of the developed theory and guidelines will then be translated into tools that both recommend visualizations that preserve privacy and support the authoring and refining of privacy-aware visualizations. Further, the team will develop educational materials at both the college and high school level that leverage the findings and tools created in the project to raise awareness of privacy in visualization,This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.