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Adaptive privacy-preserving visualization using parallel coordinates
Aritra Dasgupta1, Robert Kosara
1UNC-Charlotte, USA. adasgupt@uncc.edu
IEEE Transactions on Visualization and Computer Graphics
|October 29, 2011
Summary
This study introduces an adaptive privacy-preserving technique for parallel coordinates visualizations. It balances data utility and privacy by on-the-fly clustering, adapting to user interaction and screen parameters.
Area of Science:
- Computer Science
- Data Mining
- Information Visualization
Background:
- Information visualization often assumes unrestricted data access, posing privacy challenges for sensitive datasets like medical records.
- Existing privacy-preserving data publishing methods often reduce data utility significantly for even minimal privacy guarantees.
Purpose of the Study:
- To propose an adaptive privacy preservation technique for parallel coordinates visualizations.
- To enable exploration of sensitive data without compromising privacy or significantly reducing data utility.
Main Methods:
- Developed an adaptive technique for privacy preservation in parallel coordinates.
- Implemented on-the-fly data clustering based on data sensitivity.
- Utilized screen-space privacy metrics to adapt to user interaction and display parameters.
Main Results:
- Demonstrated a case study showcasing the proposed technique.
- The method allows for data exploration while maintaining privacy.
- The adaptive approach aims to preserve data utility.
Conclusions:
- The proposed adaptive technique offers a novel approach to privacy preservation in information visualization.
- It effectively balances data utility and privacy concerns in parallel coordinates.
- Further analysis of potential attack scenarios is discussed.
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