Research Article
A Visual Tool for Interactively Privacy Analysis and Preservation on Order-Dynamic Tabular Data
@INPROCEEDINGS{10.1007/978-3-031-24386-8_2, author={Fengzhou Liang and Fang Liu and Tongqing Zhou}, title={A Visual Tool for Interactively Privacy Analysis and Preservation on Order-Dynamic Tabular Data}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 18th EAI International Conference, CollaborateCom 2022, Hangzhou, China, October 15-16, 2022, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2023}, month={1}, keywords={Interactive system Visualization Privacy analysis}, doi={10.1007/978-3-031-24386-8_2} }- Fengzhou Liang
Fang Liu
Tongqing Zhou
Year: 2023
A Visual Tool for Interactively Privacy Analysis and Preservation on Order-Dynamic Tabular Data
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-031-24386-8_2
Abstract
The practice of releasing individual data, usually in tabular form, is obligated to prevent privacy leakage. With rendered privacy risks, visualization techniques have greatly prompted the user-friendly data sanitization process. Yet, we point out, for the first time, the attribute order (i.e., schema) of tabular data inherently determines the risk situation and the output utility, while is ignored in previous efforts. To mitigate this gap, this work proposes the design and pipeline of a visual tool (TPA, Tabular Privacy Assistant) for nuanced privacy analysis and preservation on order-dynamic tabular data. By adapting data cube structure as the flexible backbone, TPA manages to support real-time risk analysis in response to attribute order adjustment. Novel visual designs, i.e., data abstract, risk tree, integrated privacy enhancement, are developed to explore data correlations and acquire privacy awareness. We demonstrate TPA’s effectiveness with a case study on the prototype and qualitatively discuss the pros and cons with domain experts for future improvement.
