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...

• Type: Conference
• Year: 2005
• Where: CIKM
• Authors: Hui Xiong, Michael Steinbach, Vipin Kumar
• Keywords: CIKM 2005, Database Security, Privacy Leakage, Semi-supervised Learning