Privacy preservation for training datasets in database: application to decision tree learning

Privacy preservation for training datasets in database: application to decision tree learning

Privacy preservation is important for machine learning and datamining, but measures designed to protect private information sometimes result in a trade off: reduced utility of the training samples. This thesis introduces a privacy preserving approach that can be applied to decision-tree learning, w...

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Bibliographic Details
Main Author: Fong, Pui Kuen
Other Authors: Weber, Jens H.
Language:English
en
Published: 2008
Subjects:
Database
Data-mining
Decision-tree
Privacy Preservation
Mechine-learning
ID3
Data Perturbation
Entropy
Data Complementation
Dataset
Accuracy
UVic Subject Index::Sciences and Engineering::Applied Sciences::Computer science
Online Access:http://hdl.handle.net/1828/1291

Internet

http://hdl.handle.net/1828/1291

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