Adaptive privacy-preserving visualization using parallel coordinates

Aritra Dasgupta, Robert Kosara

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Current information visualization techniques assume unrestricted access to data. However, privacy protection is a key issue for a lot of real-world data analyses. Corporate data, medical records, etc. are rich in analytical value but cannot be shared without first going through a transformation step where explicit identifiers are removed and the data is sanitized. Researchers in the field of data mining have proposed different techniques over the years for privacy-preserving data publishing and subsequent mining techniques on such sanitized data. A well-known drawback in these methods is that for even a small guarantee of privacy, the utility of the datasets is greatly reduced. In this paper, we propose an adaptive technique for privacy preser vation in parallel coordinates. Based on knowledge about the sensitivity of the data, we compute a clustered representation on the fly, which allows the user to explore the data without breaching privacy. Through the use of screen-space privacy metrics, the technique adapts to the user's screen parameters and interaction. We demonstrate our method in a case study and discuss potential attack scenarios.

Original languageEnglish (US)
Article number6064989
Pages (from-to)2241-2248
Number of pages8
JournalIEEE Transactions on Visualization and Computer Graphics
Volume17
Issue number12
DOIs
StatePublished - 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Keywords

  • Parallel coordinates
  • clustering.
  • privacy

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