C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
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
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Privacy Preserving Data Mining: Challenges and Opportunities
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Hiding Association Rules by Using Confidence and Support
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Privacy Preserving Association Rule Mining
RIDE '02 Proceedings of the 12th International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems (RIDE'02)
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Privacy preserving itemset mining through noisy items
Expert Systems with Applications: An International Journal
Faking contextual data for fun, profit, and privacy
Proceedings of the 8th ACM workshop on Privacy in the electronic society
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This work investigates the problem of privacy-preserving mining of association rules. Specifically, a fake transaction randomization method is presented to protect the privacy of data. This method ensures the privacy of data by mixing real transactions with fake transactions. An algorithm for reconstructing frequent itemsets from the mixture of both fake transactions and real transactions is then proposed. The proposed algorithm can use any off-the-shelf tool to mine frequent itemsets without rewriting their codes, making this algorithm quite easy to implement. The performance of this algorithm is validated against several real datasets. Similar to earlier approaches, this algorithm does not always reconstruct a set of frequent itemsets that is anti-monotonic. This work uses this property to further improve the quality of the mining results.