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CIKM
2005
Springer

Privacy leakage in multi-relational databases via pattern based semi-supervised learning

11 years 7 months ago
Privacy leakage in multi-relational databases via pattern based semi-supervised learning
In multi-relational databases, a view, which is a context- and content-dependent subset of one or more tables (or other views), is often used to preserve privacy by hiding sensitive information. However, recent developments in data mining present a new challenge for database security even when traditional database security techniques, such as database access control, are employed. This paper presents a data mining framework using semi-supervised learning that demonstrates the potential for privacy leakage in multi-relational databases. Many different types of semi-supervised learning techniques, such as the K-nearest neighbor (KNN) method, can be used to demonstrate privacy leakage. However, we also introduce a new approach to semi-supervised learning, hyperclique pattern based semi-supervised learning (HPSL), which differs from traditional semi-supervised learning approaches in that it considers the similarity among groups of objects instead of only pairs of objects. Our experiment...
Hui Xiong, Michael Steinbach, Vipin Kumar
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where CIKM
Authors Hui Xiong, Michael Steinbach, Vipin Kumar
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