Reidentification and k-anonymity: a model for disclosure risk in graphs
2012 (English)In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 16, no 10, 1657-1670 p.Article in journal (Refereed) Published
In this article we provide a formal framework for reidentification in general. We define n-confusion as a concept for modeling the anonymity of a database table and we prove that n-confusion is a generalization of k-anonymity. After a short survey on the different available definitions of k-anonymity for graphs we provide a new definition for k-anonymous graph, which we consider to be the correct definition. We provide a description of the k-anonymous graphs, both for the regular and the non-regular case. We also introduce the more flexible concept of (k, l)-anonymous graph. Our definition of (k, l)-anonymous graph is meant to replace a previous definition of (k, l)-anonymous graph, which we here prove to have severe weaknesses. Finally, we provide a set of algorithms for k-anonymization of graphs.
Place, publisher, year, edition, pages
Springer, 2012. Vol. 16, no 10, 1657-1670 p.
anonymity, data privacy, graph
Computer Science Information Systems Discrete Mathematics
IdentifiersURN: urn:nbn:se:his:diva-10680DOI: 10.1007/s00500-012-0850-4ISI: 000308532700002ScopusID: 2-s2.0-84866126835OAI: oai:DiVA.org:his-10680DiVA: diva2:833323