kACTUS 2 : Privacy preserving in classification tasks using k-Anonymity

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KISILEVICH, Vachislav, Yuval ELOVICI, Bracha SHAPIRA, Lior ROKACH, 2009. kACTUS 2 : Privacy preserving in classification tasks using k-Anonymity. In: GAL, Cecilia S., ed., Paul B. KANTOR, ed., Michael E. LESK, ed.. Protecting Persons While Protecting the People. Berlin, Heidelberg:Springer Berlin Heidelberg, pp. 63-81. ISBN 978-3-642-10232-5

@inproceedings{Kisilevich2009kACTU-19200, title={kACTUS 2 : Privacy preserving in classification tasks using k-Anonymity}, year={2009}, doi={10.1007/978-3-642-10233-2_7}, number={5661}, isbn={978-3-642-10232-5}, address={Berlin, Heidelberg}, publisher={Springer Berlin Heidelberg}, series={Lecture Notes in Computer Science}, booktitle={Protecting Persons While Protecting the People}, pages={63--81}, editor={Gal, Cecilia S. and Kantor, Paul B. and Lesk, Michael E.}, author={Kisilevich, Vachislav and Elovici, Yuval and Shapira, Bracha and Rokach, Lior} }

2012-05-03T08:25:52Z 2012-05-03T08:25:52Z First publ. in: Protecting persons while protecting the people : Second Annual Workshop on Information Privacy and National Security, ISIPS 2008, New Brunswick, NJ, USA, May 12, 2008 / Gal, Cecilia ... (Eds.). - Berlin : Springer, 2009. - pp. 63-81. - (Lecture Notes in Computer Science ; 5661). - ISBN 978-3-642-10233-2 2009 Rokach, Lior Kisilevich, Vachislav Kisilevich, Vachislav deposit-license eng Rokach, Lior Elovici, Yuval Elovici, Yuval Shapira, Bracha Shapira, Bracha kACTUS 2 : Privacy preserving in classification tasks using k-Anonymity 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.<br />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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