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Using Sample Size to Limit Exposure to Data Mining
2000 Award Winner
Chris Clifton, The MITRE Corporation
ABSTRACT
Data mining introduces new problems in database security. The basic problem of using non-sensitive data to infer sensitive data is made more difficult by the "probabilistic" inferences possible with data mining. This paper shows how lower bounds from pattern recognition theory can be used to determine sample sizes where data mining tools cannot obtain reliable results.
Publication
Published in 2000. Journal of Computer Security, Vol. 8, pp. 281-307.
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