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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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@misc{ -unknown,
  title = "Unknown",
  url = "citeseer.ist.psu.edu/593530.html" }
Citations (may not include all citations):
1213   The self organizing map (context) - Kohonen - 1990
348   Estimation of dependences based on empirical data (context) - Vapnik - 1982
296   A Probabilistic Theory of Pattern Recognition (context) - Devroye, Gy et al. - 1996
84   Feature subset selection using a genetic algorithm - Yang, Honavar - 1998
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
7   Aerie: An inference modeling and detection approach for data.. (context) - Hinke, Delugach - 1992
5   The design and implementation of a data level database infer.. - Yip, Levitt - 1998
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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