A Similarity Measure for Sequences of Categorical Data Based on the Ordering of Common Elements
MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
A network aware privacy model for online requests in trajectory data
Data & Knowledge Engineering
Hiding co-occurring frequent itemsets
Proceedings of the 2009 EDBT/ICDT Workshops
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
Utility-preserving transaction data anonymization with low information loss
Expert Systems with Applications: An International Journal
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The process of discovering relevant patterns holding in a database, was first indicated as a threat to database security by O' Leary in [20]. Since then, many different approaches for knowledge hiding have emerged over the years, mainly in the context of association rules and frequent itemsets mining. Following many real-world data and applications demands, in this paper we shift the problem of knowledge hiding to contexts where both the data and the extracted knowledge have a sequential structure. We provide problem statement, some theoretical issues including NP-hardness of the problem, a polynomial sanitization algorithm and an experimental evaluation. Finally we discuss possible extensions that will allow to use this work as a basic building block for more complex kinds of patterns and applications.