Editors’ Choice: Privacy-Preserving Visualization

The point of visualization is usually to reveal as much of the structure of a dataset as possible. But what if the data is sensitive or proprietary, and the person doing the analysis is not supposed to be able to know everything about it? In a paper to be presented next week at InfoVis, my Ph.D. student Aritra Dasgupta and I describe the issues involved in privacy-preserving visualization, and propose a variation of parallel coordinates that controls the amount of information shown to the user.

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This content was selected for Digital Humanities Now by Editor-in-Chief based on nominations by Editors-at-Large: