Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy preserving mining of association rules
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
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
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Hiding Sensitive Patterns in Association Rules Mining
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
A Novel Method for Protecting Sensitive Knowledge in Association Rules Mining
COMPSAC '05 Proceedings of the 29th Annual International Computer Software and Applications Conference - Volume 01
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
A Max-Min Approach for Hiding Frequent Itemsets
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Preserving Private Knowledge in Frequent Pattern Mining
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A unified framework for protecting sensitive association rules in business collaboration
International Journal of Business Intelligence and Data Mining
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Privately detecting bursts in streaming, distributed time series data
Data & Knowledge Engineering
A log-linear approach to mining significant graph-relational patterns
Data & Knowledge Engineering
A rigorous and customizable framework for privacy
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
Pufferfish: A framework for mathematical privacy definitions
ACM Transactions on Database Systems (TODS)
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Discovering frequent patterns in large databases is one of the most studied problems in data mining, since it can yield substantial commercial benefits. However, some sensitive patterns with security considerations may compromise privacy. In this paper, we aim to determine appropriate balance between need for privacy and information discovery in frequent patterns. A novel method to modify databases for hiding sensitive patterns is proposed in this paper. Multiplying the original database by a sanitization matrix yields a sanitized database with private content. In addition, two probabilities are introduced to oppose against the recovery of sensitive patterns and to reduce the degree of hiding non-sensitive patterns in the sanitized database. The complexity analysis and the security discussion of the proposed sanitization process are provided. The results from a series of experiments performed to show the efficiency and effectiveness of this approach are described.