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

Kisilevich, Slava; Elovici, Yuval; Shapira, Bracha; Rokach, Lior

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

Files

Kisilevich_kACTUS.pdf(6.97 MB)

Date

2009

Authors

Kisilevich, Slava

Elovici, Yuval

Shapira, Bracha

Rokach, Lior

Publication type

Contribution to a conference collection

Published in

Protecting Persons While Protecting the People / Gal, Cecilia S.; Kantor, Paul B.; Lesk, Michael E. (ed.). - Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. - (Lecture Notes in Computer Science ; 5661). - pp. 63-81. - ISBN 978-3-642-10232-5

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.

Subject (DDC)

004 Computer Science

Keywords

anonymity,privacy preserving,generalization,suppression,data mining

Cite This

ISO 690

KISILEVICH, Slava, 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. Available under: doi: 10.1007/978-3-642-10233-2_7

BibTex

@inproceedings{Kisilevich2009kACTU-19200,
  year={2009},
  doi={10.1007/978-3-642-10233-2_7},
  title={kACTUS 2 : Privacy preserving in classification tasks using k-Anonymity},
  number={5661},
  isbn={978-3-642-10232-5},
  publisher={Springer Berlin Heidelberg},
  address={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, Slava and Elovici, Yuval and Shapira, Bracha and Rokach, Lior}
}

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