Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1991
Title: Anonymity in Multi-Instance Micro-Data Publication
Authors: Abul, Osman
31072
Keywords: Data privacy
algorithms
sensitive attributes
Issue Date: 2014
Publisher: SPRINGER
Source: Abul, O. (2013). Anonymity in Multi-Instance Micro-Data Publication. In Information Sciences and Systems 2013 (pp. 325-337). Springer, Cham.
Abstract: In this paper we study the problem of anonymity in multi-instance (MI) micro-data publication. The classical k-anonymity approach is shown to be insufficient and/or inappropriate for MI databases. Thus, it is extended to MI databases, resulting in a more general setting of MI k-anonymity. We show that MI k-anonymity problem is NP-Hard and the attack model for MI databases is different from that of single-instance databases. We make an observation that the introduced MI k-anonymity is not a strong privacy guarantee when anonymity sets are highly unbalanced with respect to instance counts. To this end a new anonymity principle, called p-certainty, which is unique to MI case is introduced. Aclustering algorithms solving the p-certainty anonymity principle is developed and experimentally evaluated.
Description: 28th International Symposium on Computer and Information Sciences (2013 : Paris; France)
URI: https://link.springer.com/chapter/10.1007%2F978-3-319-01604-7_32
https://hdl.handle.net/20.500.11851/1991
ISSN: 1876-1100
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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