Privacy-Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities

Kaustav Bhattacharjee, Min Chen, Aritra Dasgupta

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Preservation of data privacy and protection of sensitive information from potential adversaries constitute a key socio-technical challenge in the modern era of ubiquitous digital transformation. Addressing this challenge needs analysis of multiple factors: algorithmic choices for balancing privacy and loss of utility, potential attack scenarios that can be undertaken by adversaries, implications for data owners, data subjects, and data sharing policies, and access control mechanisms that need to be built into interactive data interfaces. Visualization has a key role to play as part of the solution space, both as a medium of privacy-aware information communication and also as a tool for understanding the link between privacy parameters and data sharing policies. The field of privacy-preserving data visualization has witnessed progress along many of these dimensions. In this state-of-the-art report, our goal is to provide a systematic analysis of the approaches, methods, and techniques used for handling data privacy in visualization. We also reflect on the road-map ahead by analyzing the gaps and research opportunities for solving some of the pressing socio-technical challenges involving data privacy with the help of visualization.

Original languageEnglish (US)
Pages (from-to)675-692
Number of pages18
JournalComputer Graphics Forum
Volume39
Issue number3
DOIs
StatePublished - Jun 1 2020

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design

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