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Abstract: Data mining introduces new problems in database security. The basic problem of using non-sensitive data to infer sensitive data is made more difficultby 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. (Update)
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BibTeX entry: (Update)
@misc{ -unknown,
title = "Unknown",
url = "citeseer.ist.psu.edu/593530.html" }
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296 A Probabilistic Theory of Pattern Recognition (context) - Devroye, Gy et al. - 1996
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23 Secure statistical databases with random sample queries (context) - Denning - 1980
12 Lower bounds in pattern recognition and learning (context) - Devroye, Lugosi - 1995
11 Wizard: A database inference analysis and detection system (context) - Delugach, Hinke - 1996
8 Protecting databases from inference attacks (context) - Hinke, Delugach et al. - 1997
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4 DEXTER: A system that experiments with choices of training d.. (context) - Cohen, Kulikowski et al. - 1995
2 Impact of decision-region based classification algorithms on.. (context) - Johnsten, Raghavan - 1999
1 numerical inference on statistical databases (context) - Chowdhury, Duncan et al. - 1996
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