Privacy Preservation Decision Tree Learning of Functional Dependency Dataset

Provided by: International Journal of Advanced Research in Computer Science & Technology (IJARCST)
Topic: Data Management
Format: PDF
Privacy-preserving is an important issue in the areas of data mining and security. The aim of privacy preserving data mining is to develop algorithms to modify the original dataset so that the privacy of confidential information remains preserved and as such, no confidential information could be revealed as a result of applying data mining tasks. In existing system they introduced a new privacy preserving approach via data set complementation which confirms the utility of training data sets for decision tree learning. This approach converts the original data sets, TS, into some unreal data sets such that any original data set is not able to reconstruct if an unauthorized party were steal some portion of unrealized datasets.

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