Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th 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
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 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)
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Mining association rules with non-uniform privacy concerns
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Retention replacement in privacy preserving classification
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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As a result of advances in technology, large amounts of data can be collected and stored automatically. Significant development of the Internet and easier access to it have contributed to collecting large amounts of information about users' characteristics. Along with these changes, concerns about privacy of data have emerged. Several methods of preserving privacy for association rules mining have been proposed in literature: MASKscheme and its optimizations. This paper provides new solutions concerning efficiency for this scheme and considers different methods of distorting data using randomization techniques. Effectiveness of these solutions has been tested and presented in this paper.