An Efficient KNN Classification by using Combination of Additive and Multiplicative Data Perturbation for Privacy Preserving Data Mining
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2015 by IJETT Journal|
|Year of Publication : 2015|
|Authors : Bhupendra Kumar Pandya, Umesh kumar Singh, Keerti Dixit|
|DOI : 10.14445/22315381/IJETT-V22P261|
Bhupendra Kumar Pandya, Umesh kumar Singh, Keerti Dixit"An Efficient KNN Classification by using Combination of Additive and Multiplicative Data Perturbation for Privacy Preserving Data Mining", International Journal of Engineering Trends and Technology (IJETT), V22(7),290-295 April 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe. Recently, there has been a growing interest in the data mining area, where the objective is the discovery of knowledge that is correct and of high benefit for users. Data miming consists of a set of techniques that can be used to extract relevant and interesting knowledge from data. Data mining has several tasks such as association rule mining, classification and prediction, and clustering. Classification techniques are supervised learning techniques that classify data item into predefined class label. It is one of the most useful techniques in data mining to build classification models from an input data set. The used classification techniques commonly build models that are used to predict future data trends. In this research paper we analysis CAMDP (Combination of Additive and Multiplicative Data Perturbation) technique for KNN classification as a tool for privacy-preserving data mining. We can show that KNN Classification algorithm can be efficiently applied to the transformed data and produce exactly the same results as if applied to the original data.
 Strehl A?Ghosh J. Relationship-based clustering and visualization for high-dimensional data mining[J].INFORMS J COMPUT?2003?15(2):208-230.
 Milenova B.L. ? Campos M.M.O-Cluster: scalable clustering of large high dimensional data sets[C].IEEE International Conference on Data Mining?2002?290- 297.
 B. Pandya,U.K.Singh and K. Dixit, “An Analysis of Euclidean Distance Presrving Perturbation for Privacy Preserving Data Mining” International Journal for Research in Applied Science and Engineering Technology, Vol. 2, Issue X, 2014.
 B. Pandya,U.K.Singh and K. Dixit, “Performance of Euclidean Distance Presrving Perturbation for K-Nearest Neighbour Classification” International Journal of Computer Application, Vol. 105, No. 2, pp 34-36, 2014.
 J. Han and M. Kamber. Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, San Diego, CA 92101-4495,USA, 2001.
 B. Pandya,U.K.Singh and K. Dixit, “An Evaluation of Projection Based Multiplicative Data Perturbation for KNearest Neighbour Classification” International Journal of Science and Research, Vol. 3, Issue. 12, pp 681-684, 2014.
CAMDP, KNN classification.