2005 | OriginalPaper | Chapter
k-Anonymous Patterns
Authors : Maurizio Atzori, Francesco Bonchi, Fosca Giannotti, Dino Pedreschi
Published in: Knowledge Discovery in Databases: PKDD 2005
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.