2015 (English)In: Modeling Decisions for Artificial Intelligence: 12th International Conference, MDAI 2015, Skövde, Sweden, September 21–23, 2015: Proceedings / [ed] Vicenç Torra & Yasuo Narukawa, Springer, 2015, 207-218 p.Conference paper (Refereed)
Microaggregation is an anonymization technique consistingon partitioning the data into clusters no smaller thankelements andthen replacing the whole cluster by its prototypical representant. Mostof microaggregation techniques work on numerical attributes. However,many data sets are described by heterogeneous types of data, i.e., nu-merical and categorical attributes. In this paper we propose a new mi-croaggregation method for achieving a compliantk-anonymous maskedfile for categorical microdata based on generalization. The goal is to builda generalized description satisfied by at leastkdomain objects and toreplace these domain objects by the description. The way to constructthat generalization is similar that the one used in growing decision trees.Records that cannot be generalized satisfactorily are discarded, thereforesome information is lost. In the experiments we performed we prove thatthe new approach gives good results.
Place, publisher, year, edition, pages
Springer, 2015. 207-218 p.
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 9321
IdentifiersURN: urn:nbn:se:his:diva-13358DOI: 10.1007/978-3-319-23240-9_17ScopusID: 2-s2.0-84945545104ISBN: 978-3-319-23239-3 (print)ISBN: 978-3-319-23240-9 (electronic)OAI: oai:DiVA.org:his-13358DiVA: diva2:1071374
12th International Conference, MDAI 2015, Skövde, Sweden, September 21–23, 2015