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2006

Quantification of a Privacy Preserving Data Mining Transformation

10 years 1 months ago
Quantification of a Privacy Preserving Data Mining Transformation
Data mining, with its promise to extract valuable, previously unknown and potentially useful patterns or knowledge from large data sets that contain private information is vulnerable to misuse. To protect the private or sensitive information, many privacypreserving data mining (PPDM) techniques have emerged. A large fraction of these techniques use randomized data distortion by adding noise from a known distribution function (e.g., uniform, normal) to the sensitive data. However, non-careful noise addition may introduce biases to the statistical parameters of these data. To preserve the statistical properties and meet privacy requirements of the sensitive data, we use a data transformation technique called Rotation
Mohammed Ketel
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where DMIN
Authors Mohammed Ketel
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