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Survey on Fuzzy Logic Classification over Semantically Secured Encrypted Data


P.Hemalatha , Gobi Arts & Science College ; Dr.S.M.Jagatheesan, Gobi Arts & Science College


k-NN Classifier, Encryption, Security, Fuzzy Logic


Data mining is the method of extraction of hidden and useful information from huge data. It is a knowledge domain subfield of computer science and the computational process of discovering patterns in massive data sets. Classification is one of the ordinarily used tasks in data mining applications. It is used to predict predict membership for data instances. For the past decade, due to the increase of various privacy problems, many theoretical and practical solutions to the classification drawback have been proposed under completely different security models. However, with the recent popularity of cloud computing, users currently have the opportunity to outsourced their data, in encrypted form, as well as the data mining tasks to the cloud. In existing the k-Nearest Neighbor (k-NN) classifier is used to encrypted data or information within the cloud. The k-NN classifier has less efficiency when compared to the fuzzy logic classifier. The protocols are used in the existing k-NN classifier has less efficiency. The proposed fuzzy logic classifier protects the high confidentiality of information, privacy of users input query, and hides the data access patterns. This paper reviews the cost and efficiency of the fuzzy logic classifier with k-NN classifier.

Other Details

Paper ID: IJSRDV4I60437
Published in: Volume : 4, Issue : 6
Publication Date: 01/09/2016
Page(s): 1008-1009

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