Abstract
k-anonymity is the method used for masking sensitive data which successfully solves the problem of re-linking of data with an external source and makes it difficult to re-identify the individual. Thus k-anonymity works on a set of quasi-identifiers (public sensitive attributes), whose possible availability and linking is anticipated from external dataset, and demands that the released dataset will contain at least k records for every possible quasi-identifier value. Another aspect of k is its capability of maintaining the truthfulness of the released data (unlike other existing methods). This is achieved by generalization, a primary technique in k-anonymity. Generalization consists of generalizing attribute values and substituting them with semantically consistent but less precise values. When the substituted value doesn’t preserve semantic validity the technique is called suppression which is a private case of generalization. We present a hybrid approach called compensation which is based on suppression and swapping for achieving privacy. Since swapping decreases the truthfulness of attribute values there is a tradeoff between level of swapping (information truthfulness) and suppression (information loss) incorporated in our algorithm.
We use k-anonymity to explore the issue of anonymity preservation. Since we do not use generalization, we do not need a priori knowledge of attribute semantics. We investigate data anonymization in the context of classification and use tree properties to satisfy k-anonymization. Our work improves previous approaches by increasing classification accuracy.
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References
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002)
Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression (2002)
Iyengar, V.S.: Transforming data to satisfy privacy constraints. In: KDD 2002: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 279–288. ACM, New York (2002)
Wang, K., Yu, P.S., Chakraborty, S.: Bottom-up generalization: a data mining solution to privacy protection. In: Proc. of the 4th IEEE International Conference on Data Mining (ICDM 2004) (November 2004)
Fung, B.C.M., Wang, K., Yu, P.S.: Top-down specialization for information and privacy preservation. In: Proc. of the 21st IEEE International Conference on Data Engineering (ICDE 2005), Tokyo, Japan, April 2005, pp. 205–216 (2005)
LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: efficient full-domain k-anonymity. In: SIGMOD 2005: Proceedings of the 2005 ACM SIGMOD international conference on Management of data, pp. 49–60. ACM, New York (2005)
Friedman, A., Schuster, A., Wolff, R.: k-anonymous decision tree induction. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 151–162. Springer, Heidelberg (2006)
Fung, B.C.M., Wang, K.: Anonymizing classification data for privacy preservation. IEEE Trans. on Knowl. and Data Eng. 19(5), 711–725 (2007); Fellow-Philip S. Yu
Friedman, A., Wolff, R., Schuster, A.: Providing k-anonymity in data mining. VLDB J. (2008) (accepted for publication)
Dalenius, T., Reiss, S.P.: Data-swapping, a Technique for Disclosure Control. Program in Computer Science and Division of Engineering. Brown University (1978)
Reiss, S.P.: Practical data-swapping: the first steps. ACM Trans. Database Syst. 9(1), 20–37 (1984)
Richard, A., Moore, J.: Controlled data-swapping techniques for masking public use microdata sets. Statistical Research Division Report Series, RR96-04, U.S. Bureau of the Census (1996)
Fienberg, S.E., McIntyre, J.: Data swapping: Variations on a theme by dalenius and reiss. Technical report, National Institute of Statistical Sciences, Research Triangle Park, NC (2003)
Kisilevich, S., Elovici, Y., Shapira, B., Rokach, L.: A multi-dimensional suppression for k-anonymity (to appear, 2009)
Shannon, C.E.: A mathematical theory of communication. Bell Systems Technical Journal 27, 379–423 (1948)
Newman, C.B.D., Merz, C.: UCI repository of machine learning databases (1998)
Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques with java implementations. SIGMOD Rec. 31(1), 76–77 (2002)
Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)
Salzberg, S.L.: C4.5: Programs for Machine Learning by J. Ross Quinlan. Machine Learning 16(3), 235–240 (1994)
Cessie, S.L., Houwelingen, J.C.V.: Ridge estimators in logistic regression. Applied Statistics 41(1), 191–201 (1992)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
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Kisilevich, S., Elovici, Y., Shapira, B., Rokach, L. (2009). kACTUS 2: Privacy Preserving in Classification Tasks Using k-Anonymity. In: Gal, C.S., Kantor, P.B., Lesk, M.E. (eds) Protecting Persons While Protecting the People. ISIPS 2008. Lecture Notes in Computer Science, vol 5661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10233-2_7
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DOI: https://doi.org/10.1007/978-3-642-10233-2_7
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