Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
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
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
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Detecting privacy and ethical sensitivity in data mining results
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
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
A secure distributed framework for achieving k-anonymity
The VLDB Journal — The International Journal on Very Large Data Bases
IEEE Transactions on Knowledge and Data Engineering
Hiding Sensitive Association Rules with Limited Side Effects
IEEE Transactions on Knowledge and Data Engineering
A Max-Min Approach for Hiding Frequent Itemsets
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Data reduction approach for sensitive associative classification rule hiding
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
A Heuristic Data Reduction Approach for Associative Classification Rule Hiding
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
A data perturbation approach to sensitive classification rule hiding
Proceedings of the 2010 ACM Symposium on Applied Computing
From itemsets through trajectories to location based services: a knowledge hiding privacy approach
ACM SIGKDD Explorations Newsletter
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
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Sensitive patterns could be discovered from the given data when the data are shared between business partners. Such patterns should not be disclosed to the other parties. However, the shared data should be credible and trustworthy for their 'quality'. In this paper, we address a problem of sensitive classification rule hiding by a data reduction approach. We focus on an important type of classification rules, i.e., associative classification rule. In our context, the impact on data quality generated by data reduction processes is represented by the number of false-dropped rules and ghost rules. To address the problem, we propose a few observations on the reduction approach. Subsequently, we propose a greedy algorithm for the problem based on the observations. Also, we apply two-bitmap indexes to improve the efficiency of the proposed algorithm. Experiment results are presented to show the effectiveness and the efficiency of the proposed algorithm.