Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
IEEE Transactions on Knowledge and Data Engineering
A Border-Based Approach for Hiding Sensitive Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
An integer programming approach for frequent itemset hiding
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Supporting mobile decision making with association rules and multi-layered caching
Decision Support Systems
Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns
Information Systems Research
Efficient algorithms for distortion and blocking techniques in association rule hiding
Distributed and Parallel Databases
A MaxMin approach for hiding frequent itemsets
Data & Knowledge Engineering
Knowledge sharing in virtual enterprises via an ontology-based access control approach
Computers in Industry
A model for inbound supply risk analysis
Computers in Industry
Collaborative business and data privacy: Toward a cyber-control?
Computers in Industry - Special issue: The digital factory: an instrument of the present and the future
Risk assessment and management for supply chain networks: A case study
Computers in Industry
Modeling and evaluating information leakage caused by inferences in supply chains
Computers in Industry
Data Mining: Concepts, Methods and Applications in Management and Engineering Design
Data Mining: Concepts, Methods and Applications in Management and Engineering Design
Interestingness measures for association rules: Combination between lattice and hash tables
Expert Systems with Applications: An International Journal
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Association rule hiding is an efficient solution that helps enterprises avoid the risk caused by sensitive knowledge leakage when sharing data in their collaborations. This study examines how data sharing has the potential to create risk for enterprises in retail supply chain collaboration and proposes a new algorithm to remove sensitive knowledge from the released database based on the intersection lattice of frequent itemsets. The proposed algorithm specifies the victim item such that the modification of this item causes the least impact on frequent itemsets and the non-sensitive association rule. In the experiment described in this paper, this algorithm is used in risk avoidance for a retailer sharing data in retail supply chain collaboration. The results indicate that our approach is applicable in a real context and outperforms previous mechanisms.