Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
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
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Towards low-perturbation anonymity preserving pattern discovery
Proceedings of the 2006 ACM symposium on Applied computing
Itemset frequency satisfiability: Complexity and axiomatization
Theoretical Computer Science
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Providing k-anonymity in data mining
The VLDB Journal — The International Journal on Very Large Data Bases
Towards Trajectory Anonymization: a Generalization-Based Approach
Transactions on Data Privacy
Publishing naive Bayesian classifiers: privacy without accuracy loss
Proceedings of the VLDB Endowment
Hiding co-occurring frequent itemsets
Proceedings of the 2009 EDBT/ICDT Workshops
Privacy-preserving sequential pattern release
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Practical issues on privacy-preserving health data mining
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Allowing privacy protection algorithms to jump out of local optimums: an ordered greed framework
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Relationships and data sanitization: a study in scarlet
Proceedings of the 2010 workshop on New security paradigms
Publishing anonymous survey rating data
Data Mining and Knowledge Discovery
On the identity anonymization of high-dimensional rating data
Concurrency and Computation: Practice & Experience
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In this paper we study when the disclosure of datamining results represents, per se, a threat to the anonymity of the individuals recorded in the analyzed database. The novelty of our approach is that we focus on an objective definition of privacy compliance of patterns without any reference to a preconceived knowledge of what is sensitive and what is not, on the basis of the rather intuitive and realistic constraint that the anonymity of individuals should be guaranteed. In particular, the problem addressed here arises from the possibility of inferring from the output of frequent itemset mining (i.e., a set of itemsets with support larger than a threshold ó), the existence of patterns with very low support (smaller than an anonymity threshold k)[3]. In the following we develop a simple methodology to block such inference opportunities by introducing distortion on the dangerous patterns.