Sciweavers

Share
PKDD
2005
Springer

k-Anonymous Patterns

9 years 6 months ago
k-Anonymous Patterns
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.
Maurizio Atzori, Francesco Bonchi, Fosca Giannotti
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where PKDD
Authors Maurizio Atzori, Francesco Bonchi, Fosca Giannotti, Dino Pedreschi
Comments (0)
books