Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Subgraph isomorphism in planar graphs and related problems
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Journal of the ACM (JACM)
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Algorithmics and applications of tree and graph searching
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Efficient Subgraph Isomorphism Detection: A Decomposition Approach
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Efficient Algorithm for Mining Association Rules in Large Databases
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
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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)
Protecting Sensitive Knowledge By Data Sanitization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
IEEE Transactions on Knowledge and Data Engineering
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Hiding Sensitive Patterns in Association Rules Mining
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Stack-based algorithms for pattern matching on DAGs
VLDB '05 Proceedings of the 31st international conference on Very large data bases
A Border-Based Approach for Hiding Sensitive Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns
Information Systems Research
Fg-index: towards verification-free query processing on graph databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Hiding Sensitive Trajectory Patterns
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
On the disclosure risk of multivariate microaggregation
Data & Knowledge Engineering
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
TALE: A Tool for Approximate Large Graph Matching
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
The predictive toxicology evaluation challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
A Bayesian model for disclosure control in statistical databases
Data & Knowledge Engineering
The Gaston Tool for Frequent Subgraph Mining
Electronic Notes in Theoretical Computer Science (ENTCS)
Discovering private trajectories using background information
Data & Knowledge Engineering
Extending l-diversity to generalize sensitive data
Data & Knowledge Engineering
Hiding Sequential and Spatiotemporal Patterns
IEEE Transactions on Knowledge and Data Engineering
A log-linear approach to mining significant graph-relational patterns
Data & Knowledge Engineering
Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)
Proceedings of the fifth ACM conference on Recommender systems
Fully homomorphic encryption based two-party association rule mining
Data & Knowledge Engineering
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Sensitive knowledge hiding is the problem of removing sensitive knowledge from databases before publishing. The problem is extensively studied in the context of relational databases to hide frequent itemsets and association rules. Recently, sequential pattern hiding from sequential (both sequence and spatio-temporal) databases has been investigated [1]. With the ever increasing versatile application demands, new forms of knowledge and databases should be addressed as well. In this work, we address the knowledge hiding problem in the context of tree and graph databases. For these databases efficient frequent pattern mining algorithms have already been developed in the literature. Since, some of the discovered patterns may be attributed as sensitive, we develop appropriate sanitization techniques to protect the privacy of the sensitive patterns.