Privacy leakage in multi-relational learning via unwanted classification models

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Proceedings titleProceedings of the 21st Annual International Conference on Computer Science and Software Engineering (CASCON2011)
ConferenceCASCON 2011 - The 21st Annual International Conference on Computer Science and Software Engineering, 7-10 November 2011, Toronto, Ontario, Canada
Pages4559; # of pages: 15
AbstractMultirelational classification algorithms aim to discover patterns across multiple interlinked tables in a relational database. However, when considering a complex database schema, it becomes difficult to identify all possible relationships between attributes. This is because a database often contains a very large number of attributes which come from different interconnected tables with non-determinate (such as one-to-many) relationships. A set of seemingly harmless attributes across multiple tables, therefore, may be used to learn unwanted classification models to accurately determine confidential information, leading to data leaks. Furthermore, eliminating or distorting confidential attributes may be insufficient to prevent such data disclosure, since values may be inferred based on prior insider knowledge. This paper proposes an approach to identify such "dangerous" attribute sets. For data publishing, our method generates a ranked list of subschemas which maintain the predictive performance on the class attribute, while limiting the disclosure risk, and predictive accuracy, of confidential attributes. We demonstrate the effectiveness of our method against several databases.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedYes
NPARC number21254670
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Record identifier0d862447-c4f7-4d57-bb0d-6c5042b4f352
Record created2013-02-26
Record modified2016-05-09
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