Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
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
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
An integer programming approach for frequent itemset hiding
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A MaxMin approach for hiding frequent itemsets
Data & Knowledge Engineering
Data reduction approach for sensitive associative classification rule hiding
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Hiding Sensitive Associative Classification Rule by Data Reduction
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Hiding Frequent Patterns under Multiple Sensitive Thresholds
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Hiding co-occurring frequent itemsets
Proceedings of the 2009 EDBT/ICDT Workshops
K-anonymous association rule hiding
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
k-Support anonymity based on pseudo taxonomy for outsourcing of frequent itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Associative classification rules hiding for privacy preservation
International Journal of Intelligent Information and Database Systems
Knowledge hiding from tree and graph databases
Data & Knowledge Engineering
Utility-preserving transaction data anonymization with low information loss
Expert Systems with Applications: An International Journal
Utility-guided Clustering-based Transaction Data Anonymization
Transactions on Data Privacy
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Association rule hiding in risk management for retail supply chain collaboration
Computers in Industry
Effective sanitization approaches to hide sensitive utility and frequent itemsets
Intelligent Data Analysis
| Hi-index | 0.00 |
Sharing data among organizations often leads to mutual benefit. Recent technology in data mining has enabled efficientextraction of knowledge from large databases. This, however, increases risks of disclosing the sensitive knowledge when the database is released to other parties. To address this privacy issue, one may sanitize the original database so that the sensitive knowledge is hidden. The challenge is to minimize the side effect on the quality of the sanitized database so that non-sensitive knowledge can still be mined. In this paper, we study such a problem in the context of hiding sensitive frequent itemsets by judiciously modifying the transactions in the database. To preserve the non-sensitive frequent itemsets, we propose a border-based approach to efficiently evaluate the impact of any modification to the database during the hiding process. The quality of database can be well maintained by greedily selecting the modifications with minimal side effect. Experiments results are also reported to show the effectiveness of the proposed approach.