Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Information Retrieval Systems: Theory and Implementation
Information Retrieval Systems: Theory and Implementation
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
A Graph-Based Approach for Discovering Various Types of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Protecting Respondents' Identities in Microdata Release
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
Protecting Against Data Mining through Samples
Proceedings of the IFIP WG 11.3 Thirteenth International Conference on Database Security: Research Advances in Database and Information Systems Security
Hiding Association Rules by Using Confidence and Support
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Privacy Preserving Association Rule Mining
RIDE '02 Proceedings of the 12th International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems (RIDE'02)
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
Privacy preserving mining of association rules
Information Systems - Knowledge discovery and data mining (KDD 2002)
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Data reduction approach for sensitive associative classification rule hiding
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
Hiding Frequent Patterns under Multiple Sensitive Thresholds
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
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
Hiding collaborative recommendation association rules on horizontally partitioned data
Intelligent Data Analysis
K-anonymous association rule hiding
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
A cost-efficient and versatile sanitizing algorithm by using a greedy approach
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
ρ-uncertainty: inference-proof transaction anonymization
Proceedings of the VLDB Endowment
Measuring side effects of rule hiding
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Associative classification rules hiding for privacy preservation
International Journal of Intelligent Information and Database Systems
Collusion-Free Privacy Preserving Data Mining
International Journal of Intelligent Information Technologies
Effective sanitization approaches to hide sensitive utility and frequent itemsets
Intelligent Data Analysis
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Data mining techniques have been widely used in various applications. However, the misuse of these techniques may lead to the disclosure of sensitive information. Researchers have recently made efforts at hiding sensitive association rules. Nevertheless, undesired side effects, e.g., nonsensitive rules falsely hidden and spurious rules falsely generated, may be produced in the rule hiding process. In this paper, we present a novel approach that strategically modifies a few transactions in the transaction database to decrease the supports or confidences of sensitive rules without producing the side effects. Since the correlation among rules can make it impossible to achieve this goal, in this paper, we propose heuristic methods for increasing the number of hidden sensitive rules and reducing the number of modified entries. The experimental results show the effectiveness of our approach, i.e., undesired side effects are avoided in the rule hiding process. The results also report that in most cases, all the sensitive rules are hidden without spurious rules falsely generated. Moreover, the good scalability of our approach in terms of database size and the influence of the correlation among rules on rule hiding are observed.