Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
When do data mining results violate privacy?
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Blocking Anonymity Threats Raised by Frequent Itemset Mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Effective sanitization approaches to hide sensitive utility and frequent itemsets
Intelligent Data Analysis
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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. We have recently shown [3], that the above belief is ill-founded: by shifting the concept of k-anonymity [8] from data to patterns, we have formally characterized the notion of a threat to anonymity in the context of frequent itemsets mining, and provided a methodology to efficiently and effectively identify such threats that might arise from the disclosure of a set of frequent itemsets. In our previous paper [2] we have introduced a first, naïve strategy (named suppressive) to sanitize such threats. In this paper we develop a novel sanitization strategy, named additive, which outperforms the previous one in terms of the introduced distortion and has the interesting feature of maintaining the original set of frequent itemsets unchanged, while modifying only the corresponding support values.