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
Extended Boolean information retrieval
Communications of the ACM
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Hiding Association Rules by Using Confidence and Support
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Privacy preserving frequent itemset mining
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
IEEE Transactions on Knowledge and Data Engineering
Dare to share: Protecting sensitive knowledge with data sanitization
Decision Support Systems
Hiding collaborative recommendation association rules
Applied Intelligence
Maintenance of discovered sequential patterns for record deletion
Intelligent Data Analysis
Mining association rules using clustering
Intelligent Data Analysis
Deriving Private Information from Association Rule Mining Results
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Privacy Preserving Association Rules by Using Greedy Approach
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
K-anonymous association rule hiding
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
Hybrid ensemble approach for classification
Applied Intelligence
Incrementally mining high utility patterns based on pre-large concept
Applied Intelligence
Multivariate microaggregation by iterative optimization
Applied Intelligence
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Data mining technology helps extract usable knowledge from large data sets. The process of data collection and data dissemination may, however, result in an inherent risk of privacy threats. Some sensitive or private information about individuals, businesses and organizations needs to be suppressed before it is shared or published. The privacy-preserving data mining (PPDM) has thus become an important issue in recent years. In this paper, we propose an algorithm called SIF-IDF for modifying original databases in order to hide sensitive itemsets. It is a greedy approach based on the concept borrowed from the Term Frequency and Inverse Document Frequency (TF-IDF) in text mining. The above concept is used to evaluate the similarity degrees between the items in transactions and the desired sensitive itemsets and then selects appropriate items in some transactions to hide. The proposed algorithm can easily make good trade-offs between privacy preserving and execution time. Experimental results also show the performance of the proposed approach.