Database security
Role-Based Access Control Models
Computer
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Using sample size to limit exposure to data mining
Journal of Computer Security - Special issue on database security
Flexible support for multiple access control policies
ACM Transactions on Database Systems (TODS)
Secure multi-party computation problems and their applications: a review and open problems
Proceedings of the 2001 workshop on New security paradigms
Database Management Systems
Relational Data Mining
Inductive logic programming for knowedge discovery in databases
Relational Data Mining
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Impact of Decision-Region Based Classification Mining Algorithms on Database Security
Proceedings of the IFIP WG 11.3 Thirteenth International Conference on Database Security: Research Advances in Database and Information Systems Security
An Integrated Framework for Database Privacy Protection
Proceedings of the IFIP TC11/ WG11.3 Fourteenth Annual Working Conference on Database Security: Data and Application Security, Development and Directions
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 frequent itemset mining
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A framework for privacy preserving classification in data mining
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Employing PRBAC for privacy preserving data publishing
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Privacy preserving data mining services on the web
TrustBus'05 Proceedings of the Second international conference on Trust, Privacy, and Security in Digital Business
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Recent data mining algorithms have been designed for application domains that involve several types of objects stored in multiple relations in relational databases. This fact has motivated the increasing number of successful applications of relational data mining over recent years. On the other hand, such applications have introduced a new threat to privacy and information security since from non-sensitive data one is able to infer sensitive information, including personal information, facts or even patterns that are not supposed to be disclosed. The existing access control models adopted to successfully manage the access of information in complex systems present some limitations in the context of data mining tasks. The main reason is that such models were designed to protect the access to explicit data (e.g. tables, attributes, views, etc), whereas data mining tasks deal with the discovery of implicit data (e.g. patterns). In this paper, we take a first step toward an access control model for ensuring privacy in relational data mining, notably in multi-relational association rules (MRAR). In this model, users associated with different mining access levels, even using the same algorithm, are allowed to mine different sets of association rules. We provide the groundwork to build our access control model over existing technologies and discuss some directions for future work.