Email this article Concealment Conserving the Data Mining of Groups & Individual
Abstract
We present an overview of privacy preserving data mining, one of the most popular directions in the data mining research community. In the first part of the chapter, we presented approaches that have been proposed for the protection of either the sensitive data itself in the course of data mining or the sensitive data mining results, in the context of traditional (relational) datasets. Following that, in the second part of the chapter, we focused our attention on one of the most recent as well as prominent directions in privacy preserving data mining: the mining of user mobility data. Although still in its infancy, privacy preserving data mining of mobility data has attracted a lot of research attention and already counts a number of methodologies both with respect to sensitive data protection and to sensitive knowledge hiding. Finally, in the end of the chapter, we provided some roadmap along the field of privacy preserving mobility data mining as well as the area of privacy preserving data mining at large.
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Abul, O., Atzori, M., Bonchi, F., & Giannotti, F. (2007a). Hiding Sequences. In IEEE International Conference on Data Engineering Workshop (pp. 147-156), Istanbul, Turkey.
Abul, O., Atzori, M., Bonchi, F., & Giannotti, F. (2007b). Hiding Sensitive Trajectory Patterns. In IEEE International Conference on Data Mining Workshops (pp. 693-698), Omaha, NE.
Abul, O., Bonchi, F., & Nanni, M. (2008). Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases. In International Conference on Data Engineering (pp. 376-385), Cancun, Mexico.
Aggarwal, C. C., & Yu, P. S. (Eds.). (2008). Privacy Preserving Data Mining: Models and Algorithms. New York: Springer-Verlag.
Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining Association Rules between Sets of Items in Large Databases. In ACM SIGMOD International Conference on Management of Data (pp. 207-216), Washington, DC.
Agrawal, R., & Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. In International Conference on Very Large Data Bases (pp.487-499), San Francisco, CA.
Agrawal, R., & Srikant, R. (2000). Privacy Preserving Data Mining. In ACM SIGMOD Conference on Management of Data, (pp. 439-450), Dallas, TX.
Atallah, M., Bertino, E., Elmagarmid, A. K., Ibrahim, M., & Verykios, V. S. (1999). Disclosure Limitation of Sensitive Rules. In IEEE Workshop on Knowledge and Data Engineering Exchange (pp. 45-52), Chicago, IL.
Brand, R. (2002). Microdata Protection Through Noise Addition. In Inference Control in Statistical Databases [). New York: Springer-Verlag.]. Theory into Practice, 2316, 97–116.
Cao, H., Mamoulis, N., & Cheung, D. W. (2006). Discovery of Collocation Episodes in Spatiotemporal Data. In International Conference on Data Mining (pp. 823-827), Hong Kong, China.
Chang, L., & Moskowitz, I. S. (1998). Parsimonious Downgrading and Decision Trees Applied to the Inference Problem. In Workshop on New Security Paradigms (pp. 82-89), Charlottesville, VA.
Chen, K., & Liu, L. (2005). Privacy Preserving Data Classification with Rotation Perturbation. In IEEE International Conference on Data Mining (pp. 589-592), Houston, TX.
Clifton, C. (2000). Using Sample Size to Limit Exposure to Data Mining. International Journal of Computer Security, 8(4), 281–307.
Clifton, C., Kantarcioglou, M., Lin, X., & Zhu, M. (2002). Tools for Privacy Preserving Distributed Data Mining. ACM SIGKDD Explorations, 4(2), 28–34. doi:10.1145/772862.772867
Dasseni, E., Verykios, V. S., Elmagarmid, A. K., & Bertino, E. (2001). Hiding Association Rules by Using Confidence and Support. In International Workshop on Information Hiding (pp. 369-383), Pittsburgh, PA.
Defays, D., & Nanopoulos, P. (1993). Panels of Enterprises and Confidentiality: The Small Aggregates Method. In Symposium on Design and Analysis of Longitudinal Surveys (pp. 195-204). Ottawa, Canada: Statistics Canada.
Domingo-Ferrer, J., & Mateo-Sanz, J. M. (2002). Practical Data-Oriented Microaggregation for Statistical Disclosure Control. IEEE Transactions on Knowledge and Data Engineering, 14(1), 189–201. doi:10.1109/69.979982
Domingo-Ferrer, J., & Torra, V. (2005). Ordinal, Continuous and Heterogeneous K-anonymity Through Microaggregation. Data Mining and Knowledge Discovery, 11(2), 195–212. doi:10.1007/s10618-005-0007-5
Giannotti, F., Nanni, M., & Pedreschi, D. (2006). Efficient Mining of Temporally Annotated Sequences. In SIAM International Conference on Data Mining, Bethesda, MD, (pp. 346-357).
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