Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Knowledge Discovery in Personal Data vs. Privacy: A mini-symposium
IEEE Expert: Intelligent Systems and Their Applications
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
The Security of Confidential Numerical Data in Databases
Information Systems Research
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
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
Perturbing Nonnormal Confidential Attributes: The Copula Approach
Management Science
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)
An integer programming approach for frequent itemset hiding
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Dare to share: Protecting sensitive knowledge with data sanitization
Decision Support Systems
Minimizing Information Loss and Preserving Privacy
Management Science
Identity disclosure protection: A data reconstruction approach for privacy-preserving data mining
Decision Support Systems
K-anonymous association rule hiding
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
A privacy protection technique for publishing data mining models and research data
ACM Transactions on Management Information Systems (TMIS)
Revisiting sequential pattern hiding to enhance utility
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge hiding from tree and graph databases
Data & Knowledge Engineering
Design science and the accumulation of knowledge in the information systems discipline
ACM Transactions on Management Information Systems (TMIS)
Integer linear programming for Constrained Multi-Aspect Committee Review Assignment
Information Processing and Management: an International Journal
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
| Hi-index | 0.00 |
The sharing of databases either within or across organizations raises the possibility of unintentionally revealing sensitive relationships contained in them. Recent advances in data-mining technology have increased the chances of such disclosure. Consequently, firms that share their databases might choose to hide these sensitive relationships prior to sharing. Ideally, the approach used to hide relationships should be impervious to as many data-mining techniques as possible, while minimizing the resulting distortion to the database. This paper focuses on frequent item sets, the identification of which forms a critical initial step in a variety of data-mining tasks. It presents an optimal approach for hiding sensitive item sets, while keeping the number of modified transactions to a minimum. The approach is particularly attractive as it easily handles databases with millions of transactions. Results from extensive tests conducted on publicly available real data and data generated using IBM's synthetic data generator indicate that the approach presented is very effective, optimally solving problems involving millions of transactions in a few seconds.