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Partial Domain Theories for Privacy
IIIA - Artificial Intelligence Research Institute, CSIC - Spanish Council for Scientific Research, Catalonia, Spain.
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0002-0368-8037
2016 (engelsk)Inngår i: Modeling Decisions for Artificial Intelligence: 13th International Conference, MDAI 2016 Sant Julià de Lòria, Andorra, September 19–21, 2016, Proceedings, Springer, 2016, s. 217-226Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Generalization and Suppression are two of the most used techniques to achieve k-anonymity. However, the generalization concept is also used in machine learning to obtain domain models useful for the classification task, and the suppression is the way to achieve such generalization. In this paper we want to address the anonymization of data preserving the classification task. What we propose is to use machine learning methods to obtain partial domain theories formed by partial descriptions of classes. Differently than in machine learning, we impose that such descriptions be as specific as possible, i.e., formed by the maximum number of attributes. This is achieved by suppressing some values of some records. In our method, we suppress only a particular value of an attribute in only a subset of records, that is, we use local suppression. This avoids one of the problems of global suppression that is the loss of more information than necessary.

sted, utgiver, år, opplag, sider
Springer, 2016. s. 217-226
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 9880
Emneord [en]
Machine learning, Lazy learning methods, Partial domain models, k-anonymity, Supression
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
URN: urn:nbn:se:his:diva-13304DOI: 10.1007/978-3-319-45656-0_18ISI: 000389706200018Scopus ID: 2-s2.0-84989324695ISBN: 978-3-319-45655-3 (tryckt)ISBN: 978-3-319-45656-0 (digital)OAI: oai:DiVA.org:his-13304DiVA, id: diva2:1064027
Konferanse
13th International Conference on Modeling Decisions for Artificial Intelligence (MDAI), Sant Julia de Loria, Andorra, September 19-21, 2016
Tilgjengelig fra: 2017-01-11 Laget: 2017-01-11 Sist oppdatert: 2018-03-28bibliografisk kontrollert

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