Paper Info
Title
Privacy-Preserving Data Visualization Using Parallel Coordinates
Abstract
The proliferation of data in the past decade has created demand for innovative tools in different areas of exploratory data analysis, like data mining and information visualization. However, the problem with real-world datasets is that many of their attributes can identify individuals, or the data are proprietary and valuable. The field of data mining has developed a variety of ways for dealing with such data, and has established an entire subfield for privacy-preserving data mining. Visualization, on the other hand, has seen little, if any, work on handling sensitive data. With the growing applicability of data visualization in real-world scenarios, the handling of sensitive data has become a non-trivial issue we need to address in developing visualization tools.With this goal in mind, in this paper, we analyze the issue of privacy from a visualization perspective and propose a privacy-preserving visualization technique based on clustering in parallel coordinates. We also outline the key differences in approach from the privacy-preserving data mining field and compare the advantages and drawbacks of our approach.
Year
DOI
Venue
2011
10.1117/12.872635
VISUALIZATION AND DATA ANALYSIS 2011
Keywords
Field
DocType
privacy, k-anonymity, visualization, parallel coordinates
Data science,Data visualization,Information visualization,Visualization,Computer science,k-anonymity,Visual analytics,Parallel coordinates,Exploratory data analysis,Cluster analysis
Conference
Volume
ISSN
Citations 
7868
0277-786X
3
PageRank 
References 
Authors
0.42
18
2
Name
Order
Citations
PageRank
Aritra Dasgupta117512.02
Robert Kosara2102567.46