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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

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-super vised 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 experimental results show that both the KNN and HPSL methods have the ability to compromise database security, although HPSL is better at this privacy violation than the KNN method.

Original languageEnglish (US)
Title of host publicationCIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages355-356
Number of pages2
ISBN (Print)1595931406, 9781595931405
DOIs
StatePublished - 2005
EventCIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management - Bremen, Germany
Duration: Oct 31 2005Nov 5 2005

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

OtherCIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
Country/TerritoryGermany
CityBremen
Period10/31/0511/5/05

Keywords

  • Database Security
  • Hyperclique Patterns
  • Privacy Preserving Data Mining
  • Semi-supervised Learning

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