PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Atzori M., Bonchi F., Giannotti F., Pedreschi D. k-anonymous patterns. In: European Conference on Principles and Practice of Knowledge (Porto, Portugal, October 3-7, 2005). Proceedings, vol. Vol. 3721 pp. 10 - 21. Alípio M. Jorge, Luís Torgo, Pavel B. Brazdil, Rui Camacho, João Gama (eds.). (Lecture Notes in Control and Information Sciences). Springer-Verlag GmbH, 2005.
 
 
Abstract
(English)
It is generally believed that data mining results do not violate the anonymity of the individuals recorded in the source database. In fact, data mining models and patterns, in order to ensure a required statistical significance, represent a large number of individuals and thus conceal individual identities: this is the case of the minimum support threshold in association rule mining. In this paper we show that this belief is ill-founded. By shifting the concept of k-anonymity from data to patterns, we formally characterize the notion of a threat to anonymity in the context of pattern discovery, and provide a methodology to efficiently and effectively identify all possible such threats that might arise from the disclosure of a set of extracted patterns.
URL: http://www.di.unipi.it/~atzori/
Subject Data mining
Frequent Patterns Mining
Data Privacy
Algorithms
H.2.8 Database Applications. Data mining


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