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Adaptive Privacy-Preserving Visualization Using Parallel Coordinates
Dec. 2011 (vol. 17 no. 12)
pp. 2241-2248
Aritra Dasgupta, UNC-Charlotte
Robert Kosara, UNC-Charlotte
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.

[1] G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy, D. Thomas, and A. Zhu, Approximation algorithms for k-anonymity. In Journal of Privacy Technology, 2005.
[2] R. Agrawal and R. Srikant, Privacy-preserving data mining. ACM Sigmod Record, 29 (2): 439–450, 2000.
[3] E. Bertino, D. Lin, and W. Jiang, A Survey of Quantification of Privacy Preserving Data Mining Algorithms. Privacy-Preserving Data Mining, pages 183–205, 2008.
[4] M. Bezzi, An entropy based method for measuring anonymity. In Third International Conference on Security and Privacy in Communications Networks and the Workshops, 2007., pages 28–32. IEEE, 2008.
[5] J. Brickell and V. Shmatikov, The cost of privacy. Knowledge discovery and data mining, 2008.
[6] S. Bu, L. V. Lakshmanan, R. T. Ng, and G. Ramesh, Preservation Of Patterns and Input-Output Privacy. 23rd International Conference on Data Engineering, pages 696–705, Apr. 2007.
[7] J. Byun, A. Kamra, E. Bertino, and N. Li, Efficient k-anonymization using clustering techniques. In Proceedings Database Systems for Advanced Applications, pages 188–200. Springer, 2007.
[8] C. Clifton, M. Kantarcioglu, and J. Vaidya, Defining privacy for data mining. In NSF Workshop on Next Generation Data Mining, pages 126– 133, 2002.
[9] A. Dasgupta and R. Kosara, Pargnostics: screen-space metrics for parallel coordinates. IEEE Transactions on Visualization and Computer Graphics, 16 (6): 1017–26, 2010.
[10] A. Dasgupta and R. Kosara, Privacy-preserving data visualization using parallel coordinates. In Proceedings Visualization and Data Analysis (VDA), pages 78680O–1–78680O–12, 2011.
[11] G. T. Duncan and D. Lambert, Disclosure-limited data dissemination. Journal of the American Statistical Assn., 81 (393): pp. 10–18, 1986.
[12] C. Dwork, Differential privacy. In ICALP, pages 1–12. Springer, 2006.
[13] A. Frank and A. Asuncion, UCI machine learning repository. http://archive.ics.uci.eduml, 2010.
[14] Y.-H. Fua, M. O. Ward, and E. A. Rundensteiner, Hierarchical parallel coordinates for exploration of large datasets. In Proceedings Visualization, pages 43–50. IEEE CS Press, 1999.
[15] A. Inselberg and B. Dimsdale, Parallel coordinates: A tool for visualizing multi-dimensional geometry. In IEEE Visualization, pages 361–378. IEEE CS Press, 1990.
[16] J. Johansson, P. Ljung, M. Jern, and M. Cooper, Revealing structure within clustered parallel coordinates displays. In Proceedings Information Visualization, pages 125–132, 2005.
[17] F. D. K. El, Emam. Protecting privacy using k-anonymity. Journal of the American Medical Informatics Association, 15: 627–637, 2008.
[18] D. Lambert, Measures of disclosure risk and harm. Journal of Official Statistics, 9: 313–331, 1993.
[19] J. Li, J.-B. Martens, and J. J. van Wijk, Judging correlation from scatter-plots and parallel coordinate plots. Information Visualization, 9 (1): 13–30, 2010.
[20] P. Luzzardi, C. Freitas, R. Cava, G. Duarte, and M. Vasconcelos, An Extended Set of Ergonomic Criteria for Information Visualization Techniques. In Proceedings Computer Graphics And Imaging, pages 236– 241, 2004.
[21] A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, 1-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD), 1 (1): 3, 2007.
[22] A. Meyerson and R. Williams, On the complexity of optimal k-anonymity. In Proceedings Principles of Database Systems, pages 223– 228. ACM, 2004.
[23] M. Novotny and H. Hauser, Outlier-preserving focus+context visualization in parallel coordinates. IEEE Transactions on Visualization and Computer Graphics, 12 (5): 893–900, 2006.
[24] P. O'Dea, J. Griffith, and C. O'Riordan, Combining feature selection and neural networkds for solving classification problems. Irish Conference on Artifical Intelligence & Cognitive Science, pages 157–166, 2001.
[25] H. Piringer, R. Kosara, and H. Hauser, Interactive focus+context visualization with linked 2d/3d scatterplots. In Coordinated and Multiple Views in Exploratory Visualization, pages 49–60, 2004.
[26] L. Sweeney, k-Anonymity: A Model for Protecting Privacy. IEEE Security And Privacy, 10 (5): 1–14, 2002.
[27] S. F. V. Ciriani, S. De Capitani di Vimercati, and P. Samarati, k-anonymous data mining: A survey. In Privacy-Preserving Data Mining: Models and Algorithms, pages 105–136. Springer-Verlag, 2007.
[28] H. Zhou, W. Cui, H. Qu, Y. Wu, X. Yuan, and W. Zhuo, Splatting the Lines in Parallel Coordinates. Computer Graphics Forum, 28 (3): 759– 766, 2009.
[29] H. Zhou, X. Yuan, H. Qu, W. Cui, and B. Chen, Visual clustering in parallel coordinates. Computer Graphics Forum, 27 (3): 1047–1054, 2008.
[30] C. Ziemkiewicz and R. Kosara, Embedding Information Visualization Within Visual Representation. Advances in Information and Intelligent Systems, pages 307–326, 2010.

Index Terms:
Parallel coordinates, privacy, clustering.
Citation:
Aritra Dasgupta, Robert Kosara, "Adaptive Privacy-Preserving Visualization Using Parallel Coordinates," IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 12, pp. 2241-2248, Dec. 2011, doi:10.1109/TVCG.2011.163
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