Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Principles of data mining
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Detecting privacy and ethical sensitivity in data mining results
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
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
Towards low-perturbation anonymity preserving pattern discovery
Proceedings of the 2006 ACM symposium on Applied computing
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
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
Privacy-preserving frequent pattern sharing
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Privacy-preserving data mining: A feature set partitioning approach
Information Sciences: an International Journal
Relationships and data sanitization: a study in scarlet
Proceedings of the 2010 workshop on New security paradigms
Privacy-preserving publishing microdata with full functional dependencies
Data & Knowledge Engineering
Publishing anonymous survey rating data
Data Mining and Knowledge Discovery
k-Anonymous Decision Tree Induction
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
On the identity anonymization of high-dimensional rating data
Concurrency and Computation: Practice & Experience
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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. 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.