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dc.contributor.authorThaeter, Florian
dc.contributor.authorReischuk, Rüdiger
dc.contributor.editorLangweg, Hanno
dc.contributor.editorMeier, Michael
dc.contributor.editorWitt, Bernhard C.
dc.contributor.editorReinhardt, Delphine
dc.date.accessioned2018-03-22T12:40:43Z
dc.date.available2018-03-22T12:40:43Z
dc.date.issued2018
dc.identifier.isbn978-3-88579-675-6
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/16295
dc.description.abstractMicroaggregation is a technique to preserve privacy when confidential information about individuals shall be used by third parties. A basic property to be established is called k-anonymity. It requires that identifying information about individuals should not be unique, instead there has to be a group of size at least k that looks identical. This is achieved by clustering individuals into appropriate groups and then averaging the identifying information. The question arises how to select these groups such that the information loss by averaging is minimal. This problem has been shown to be NP-hard. Thus, several heuristics called MDAV, V-MDAV,... have been proposed for finding at least a suboptimal clustering. This paper proposes a more sophisticated, but still efficient strategy called MDAV* to construct a good clustering. The question whether to extend a group locally by individuals close by or to start a new group with such individuals is investigated in more depth. This way, a noticeable lower information loss can be achieved which is shown by applying MDAV* to several established benchmarks of real data and also to specifically designed random data.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSICHERHEIT 2018
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-281
dc.subjectMicrodata anonymization
dc.subjectk-Anonymity
dc.subjectMicroaggregation
dc.subjectgroup clustering
dc.titleImproving Anonymization Clusteringen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages69-82
mci.conference.sessiontitleWissenschaftliche Beiträge
mci.conference.locationKonstanz, Germany
mci.conference.date25.-27. April 2018
dc.identifier.doi10.18420/sicherheit2018_05


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