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Subverting Knowledge Discovery in Adversarial Settings

By J. G. Dutrisac and D. B. Skillicorn

Abstract

Copyright c○J.G. Dutrisac and D.B. Skillicorn Knowledge-discovery technologies are assessed by how well they model real-world situations, but little attention has been paid to how robust they are when the data they use has been deliberately manipulated. We show that it is straightforward to subvert some mainstream prediction technologies (decision trees, support vector machines), and clustering technologies (expectation-maximization and matrix decompositions) by the addition of a small number of records. The resulting models have predictable regions where records describing the existence, properties or activities of ‘bad guys ’ can be concealed from analysis. Subverting Knowledge Discovery in Adversarial Settings J.G. Dutrisac and D.B. Skillicorn Abstract: Knowledge-discovery technologies are assessed by how well they model real-world situations, but little attention has been paid to how robust they are when the data they use has been deliberately manipulated. We show that it is straightforward to subvert some mainstream prediction technologies (decision trees, support vector machines), and clustering technologies (expectation-maximization and matrix decompositions) by the addition of a small number of records. The resulting models have predictable regions where records describing the existence, properties or activities of ‘bad guys ’ can be concealed from analysis.

Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.211.6270
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