Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Leaderrship and group search in group decision support systems
Decision Support Systems
Using Association Rules as Texture Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Security of Confidential Numerical Data in Databases
Information Systems Research
The true lift model: a novel data mining approach to response modeling in database marketing
ACM SIGKDD Explorations Newsletter
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
Protecting Sensitive Knowledge By Data Sanitization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Perturbing Nonnormal Confidential Attributes: The Copula Approach
Management Science
IEEE Transactions on Knowledge and Data Engineering
The influence of communication mode and incentive structure on GDSS process and outcomes
Decision Support Systems
Impact of GDSS: opening the black box
Decision Support Systems
Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns
Information Systems Research
Image segmentation using association rule features
IEEE Transactions on Image Processing
Identity disclosure protection: A data reconstruction approach for privacy-preserving data mining
Decision Support Systems
A heuristic data-sanitization approach based on TF-IDF
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
International Journal of Information Management: The Journal for Information Professionals
How does social software change knowledge management? Toward a strategic research agenda
The Journal of Strategic Information Systems
Using TF-IDF to hide sensitive itemsets
Applied Intelligence
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Data sanitization is a process that is used to promote sharing of transactional databases among organizations while alleviating concerns of individual organizations by preserving confidentiality of their sensitive knowledge in the form of sensitive association rules. It hides the frequent itemsets corresponding to the sensitive association rules that contain sensitive knowledge by modifying the sensitive transactions that contain those itemsets. This process is guided by the need to minimize the impact on the data utility of the sanitized database by allowing mining as much as possible of the non-sensitive knowledge in the form non-sensitive association rules from the sanitized database. We propose three heuristic approaches for the sanitization problem. Results from extensive tests conducted on publicly available real datasets indicate that the approaches are effective and outperform a previous approach in terms of data utility at the expense of computational speed. The proposed approaches sanitize also the databases with great data accuracy, thus resulting in little distortion of the released databases. We recommend that the database owner sanitize the database using the third proposed hybrid approach.