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Privacy Preservation Decision Tree Based On Data Set Complementation

Madhusmita Sahu, Debasis Gountia, Neelamani Samal

Privacy preservation in data mining has been a popular and an important research area for more than a decade due to its vast spectrum of applications. A new class of data mining method called privacy preserving data mining algorithm has been developed. The aim of this algorithm is to protect the sensitive information in data from the large amount of data set. The privacy preservation of data set can be expressed in the form of decision tree, cluster or association rule. This paper proposes a privacy preservation based on data set complement algorithms which store the information of the real dataset. So that the private data can be safe from the unauthorized party, if some portion of the data can be lost, then we can reconstructed the original data set from the unrealized dataset and the perturbing data set.