Measuring Privacy and Utility in Privacy-Preserving Visualization
Article first published online: 5 JUL 2013
© 2013 The Authors Computer Graphics Forum © 2013 The Eurographics Association and John Wiley & Sons Ltd.
Computer Graphics Forum
Volume 32, Issue 8, pages 35–47, December 2013
How to Cite
Dasgupta, A., Chen, M. and Kosara, R. (2013), Measuring Privacy and Utility in Privacy-Preserving Visualization. Computer Graphics Forum, 32: 35–47. doi: 10.1111/cgf.12142
- Issue published online: 27 NOV 2013
- Article first published online: 5 JUL 2013
- visual uncertainty;
- Human-centered Computing;
In previous work, we proposed a technique for preserving the privacy of quasi-identifiers in sensitive data when visualized using parallel coordinates. This paper builds on that work by introducing a number of metrics that can be used to assess both the level of privacy and the amount of utility that can be gained from the resulting visualizations. We also generalize our approach beyond parallel coordinates to scatter plots and other visualization techniques. Privacy preservation generally entails a trade-off between privacy and utility: the more the data are protected, the less useful the visualization. Using a visually-oriented approach, we can provide a higher amount of utility than directly applying data anonymization techniques used in data mining. To demonstrate this, we use the visual uncertainty framework for systematically defining metrics based on cluster artifacts and information theoretic principles. In a case study, we demonstrate the effectiveness of our technique as compared to standard data-based clustering in the context of privacy-preserving visualization.