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 "prob abilistic" 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.
Clifton REF investigates the techniques to address the basic problem of using non-sensitive data to infer sensitive data in the context of data mining.
Using Sample Size to Limit Exposure to Data Mining
Journal of Computer Security
Computer Science