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
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th 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
Protecting Sensitive Knowledge By Data Sanitization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
IEEE Transactions on Knowledge and Data Engineering
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Hiding Sensitive Patterns in Association Rules Mining
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A Border-Based Approach for Hiding Sensitive Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Blocking Anonymity Threats Raised by Frequent Itemset Mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Hiding Sensitive Trajectory Patterns
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
A MaxMin approach for hiding frequent itemsets
Data & Knowledge Engineering
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Hiding co-occurring sensitive patterns in progressive databases
Proceedings of the 2010 EDBT/ICDT Workshops
Journal of Network and Computer Applications
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Knowledge hiding, hiding rules/patterns that are inferable from published data and attributed sensitive, is extensively studied in the literature in the context of frequent itemsets and association rules mining from transactional data. The research in this thread is focused mainly on developing sophisticated methods that achieve less distortion in data quality. With this work, we extend frequent item-set hiding to co-occurring frequent itemset hiding problem. Co-occurring frequent itemsets are those itemsets that co-exist in the output of frequent itemset mining. What is different from the classical frequent hiding is the new sensitivity definition: an itemset set is sensitive if its itemsets appear altogether within the frequent item-set mining results. In other words, co-occurrence is defined with reference to the mining results but not to the raw input dataset, and thus it is a kind of meta-knowledge. Our notion of co-occurrence is also very different from association rules as itemsets in an association rule need to be frequently present in the same set of transactions, but the co-occurrence need not necessarily require the joint occurrence in the same set of transactions. In this paper, we briefly review the frequent itemset/association hiding problems and define the co-occurrence hiding along with the real world motivations. We explore its fundamental properties and show that frequent itemset hiding is a special case of the co-occurring frequent itemsets hiding. As a solution, we propose a two-stage sanitization framework, essentially a reduction, where an instance of the frequent itemset hiding is constructed in the first stage and the instance is solved in the second stage. Since the task is shown to be NP-Hard and the reduction is one-to-many, we propose heuristics only for the first stage as the second stage is a well-established field. Finally, an experimental evaluation is carried out on a couple of datasets, and the results are presented.