Efficient Decision Tree Generation with Privacy Using Perturbation

  Nency Ghetia,  Prof. Nitin J. Rola

In recent years, advances in hardware technology have led to an increase in the capability to store and record personal data about consumers and individuals. This has led to concerns that the personal data may be misused for a variety of purposes.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. the data set complementation approach expands the sample storage size (in the worst case, the storage size equals (2|TU-1|*|TS|) ; perturbation will improve some storage size using like c5.0 algorithm.we will optimize the processing time when generating a decision tree from those samples and funtional dependencies.This paper work on optimize processing time,improve storage size,reduce processing time and functional dependencies.

Keywords- classification, data mining, secuirity,cryptography, decision tree

Unique Identification Number - IJEDR1404040
Page Number(s) - 3633-3635
Pubished in - Volume 2 | Issue 4 | Dec 2014
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Nency Ghetia,  Prof. Nitin J. Rola,   "Efficient Decision Tree Generation with Privacy Using Perturbation", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.2, Issue 4, pp.3633-3635, Dec 2014, Available at :http://www.ijedr.org/papers/IJEDR1404040.pdf