Abstract
Data anonymization aims to mitigate privacy and security concerns and to comply with legal requirements by obfuscating personal details [Fung, 2010]. In this way, data anonymization prevents an adversary from mapping sensitive information to an individual. There are three primary circumstances in which data anonymization is required:
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References
Domingo-Ferrer, J. 2008. Privacy-Preserving Data Mining: Models and Algorithms. Springer, Chapter A Survey of Inference Control Methods for Privacy-Preserving Data Mining, 53–80.
Friedman, A., Wolff, R., and Schuster, A. 2009. Providing k-anonymity in data mining. The International Journal on Very Large Data Bases, 17(4), 789–804.
Fung, B.C.M., Wang, K., and Yu, P.S. 2007. Anonymizing classification data for privacy preservation. IEEE Transactions on Knowledge and Data Engineering (TKDE) 19(5), 711–725.
Gionis, A., Mazza, A. and Tassa, T. 2008. k-anonymization revisited. Proceedings, International Conference on Data Engineering (ICDE), 744–753.
Goldberger, J. and Tassa, T. 2010. Efficient anonymizations with enhanced utility. Transactions on Data Privacy 3, 149–175.
Iyengar, V.S. 2002. Transforming data to satisfy privacy constraints. Proceedings, 8th ACM SIGKDD. Edmonton, AB, Canada, 279–288.
Nergiz, M. E. and Clifton, C. 2006. Thoughts on k-anonymization. Proceedings, International Conference on Data Engineering (ICDE) Workshops.
Samarati, P. 2001. Protecting respondents’ identities in microdata release. IEEE Transactions on Knowledge and Data Engineering (TKDE) 13(6), 1010–1027.
Sharkey, P., Tian, H., Zhang W., and Xu, S., 2008. Privacy-Preserving Data Mining Through Knowledge Model Sharing. Privacy, Security and Trust in KDD, 4890, 97–115.
Sweeney, L. 1997. Datafly: a system for providing anonymity in medical data. In Proceedings of the IFIP TC11 WG11.3 11th International Conference on Database Security XI: Status and Prospects, 356–381.
Verykios, V.S., Bertino, E., Fovino, I.N., Provenza, L.P., Saygin, Y. and Theodoridis, Y. 2004. State-of-the-art in privacy preserving data mining. ACM SIGMOD Record, 33(1), 50–57.
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Shabtai, A., Elovici, Y., Rokach, L. (2012). Privacy, Data Anonymization, and Secure Data Publishing. In: A Survey of Data Leakage Detection and Prevention Solutions. SpringerBriefs in Computer Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-2053-8_6
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DOI: https://doi.org/10.1007/978-1-4614-2053-8_6
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