Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using sample size to limit exposure to data mining
Journal of Computer Security - Special issue on database security
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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
IEEE Transactions on Knowledge and Data Engineering
Detecting privacy and ethical sensitivity in data mining results
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
Privacy preserving mining of association rules
Information Systems - Knowledge discovery and data mining (KDD 2002)
A Border-Based Approach for Hiding Sensitive Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Template-Based Privacy Preservation in Classification Problems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Hiding Sensitive Association Rules with Limited Side Effects
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A Max-Min Approach for Hiding Frequent Itemsets
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Two new techniques for hiding sensitive itemsets and their empirical evaluation
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
An approach of private classification on vertically partitioned data
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
A data perturbation approach to sensitive classification rule hiding
Proceedings of the 2010 ACM Symposium on Applied Computing
Associative classification rules hiding for privacy preservation
International Journal of Intelligent Information and Database Systems
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When a business unit shares data with another unit, there could be some sensitive patterns which should not be disclosed. In order to remove or "hide" a sensitive pattern in data sharing scenario, the data set needs to be modified such that the sensitive pattern becomes uninteresting according to the pre-specified "interestingness" threshold (s). However, data quality of the given data set should also be maintained, otherwise, the sharing will be meaningless. Existing data modification algorithms usually use data perturbation approach, i.e. changing some data values in a given data set from an original value to another value. Though, it could hide sensitive patterns and maintain data quality, such the approach could not be applied in a situation where real data are required. In this paper, we explore an alternate approach for sensitive pattern hiding problem, data reduction, i.e. removing the whole selected tuples. By data reduction, every tuple in modified data sets is real data without any change. The focused pattern type is associative classification rule. The impact on data quality is denoted as the numbers of false-dropped rules and ghost rules. The experiments are conducted to evaluate the approach and the results have shown that data reduction approach can produce data sets with high data quality, thus it is applicable to the problem.