Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
IEEE Transactions on Knowledge and Data Engineering
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
A General Model for Sequential Pattern Mining with a Progressive Database
IEEE Transactions on Knowledge and Data Engineering
Hiding co-occurring frequent itemsets
Proceedings of the 2009 EDBT/ICDT Workshops
Journal of Network and Computer Applications
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These days lot of work is been carried out in the field of privacy preserving data mining. Apart from the standard techniques of privacy preservation, methods are being proposed specific to the data mining tasks carried out. However most of these methods work on static databases. One such a method is downgrading application effectiveness. The effectiveness of applications may be downgraded by hiding sensitive association rules, hiding sensitive sets of patterns etc. Sometimes although a particular pattern is not interesting, its co-occurrence with another pattern may reveal certain sensitive information. In this paper we present a novel technique to hide sensitive co-occurring sequential patterns. The proposed method works on progressive databases. Progressive databases are a generalized model of static, dynamic and incremental databases. The applicability of the method is also extended to suit these different types of databases.