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

Lade...
Vorschaubild
Dateien
Kisilevich_kACTUS.pdf
Kisilevich_kACTUS.pdfGröße: 6.97 MBDownloads: 941
Datum
2009
Autor:innen
Elovici, Yuval
Shapira, Bracha
Rokach, Lior
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
EU-Projektnummer
DFG-Projektnummer
Angaben zur Forschungsförderung (Freitext)
Projekt
Open Access-Veröffentlichung
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
GAL, Cecilia S., ed., Paul B. KANTOR, ed., Michael E. LESK, ed.. Protecting Persons While Protecting the People. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 63-81. Lecture Notes in Computer Science. 5661. ISBN 978-3-642-10232-5. Available under: doi: 10.1007/978-3-642-10233-2_7
Zusammenfassung

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.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
anonymity, privacy preserving, generalization, suppression, data mining
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690KISILEVICH, 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, 2009, pp. 63-81. Lecture Notes in Computer Science. 5661. 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}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/19200">
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Shapira, Bracha</dc:creator>
    <dc:creator>Elovici, Yuval</dc:creator>
    <dc:contributor>Shapira, Bracha</dc:contributor>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/19200"/>
    <dc:contributor>Elovici, Yuval</dc:contributor>
    <dc:contributor>Kisilevich, Slava</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-05-03T08:25:52Z</dcterms:available>
    <dcterms:abstract xml:lang="eng">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.&lt;br /&gt;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.</dcterms:abstract>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-05-03T08:25:52Z</dc:date>
    <dc:creator>Kisilevich, Slava</dc:creator>
    <dcterms:issued>2009</dcterms:issued>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:bibliographicCitation>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</dcterms:bibliographicCitation>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:title>kACTUS 2 : Privacy preserving in classification tasks using k-Anonymity</dcterms:title>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/19200/2/Kisilevich_kACTUS.pdf"/>
    <dc:contributor>Rokach, Lior</dc:contributor>
    <dc:creator>Rokach, Lior</dc:creator>
    <dc:language>eng</dc:language>
    <dc:rights>terms-of-use</dc:rights>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/19200/2/Kisilevich_kACTUS.pdf"/>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen