Enhanced K-means clustering with encryption on cloud

Abstract

Abstract. This paper tries to solve the problem of storing and managing big files over cloud by implementing hashing on Hadoop in big-data and ensure security while uploading and downloading files. Cloud computing is a term that emphasis on sharing data and facilitates to share infrastructure and resources. [10] Hadoop is an open source software that gives us access to store and manage big files according to our needs on cloud. K-means clustering algorithm is an algorithm used to calculate distance between the centroid of the cluster and the data points. Hashing is a algorithm in which we are storing and retrieving data with hash keys. The hashing algorithm is called as hash function which is used to portray the original data and later to fetch the data stored at the specific key.[17] Encryption is a process to transform electronic data into non readable form known as cipher text. Decryption is the opposite process of encryption, it transforms the cipher text into plain text that the end user can read and understand well. For encryption and decryption we are using Symmetric key cryptographic algorithm. In symmetric key cryptography are using DES algorithm for a secure storage of the files. [3]

Clustering EDP (Error Detection Program) Errors from Cloud Data Centres using Data Mining

“…The hashing algorithm is called as hash function which is used to portray the original data and later to fetch the data stored at the specific key. [9] After execution they concluded, by adding hashing them able to access files faster and with the help of encryption, the data stored in the HDFS is safe and secure. Degloved algorithm will not create load on the overall system and smooth retrieving is enabled to the user through which he can get the desired and applicable output.…”

“…Degloved algorithm will not create load on the overall system and smooth retrieving is enabled to the user through which he can get the desired and applicable output. [9] Mr. Sudhir M. Gorade1, Prof. Ankit Deo2, Prof. Preetesh Purohit, worked on "A Study of Some Data Mining Classification Techniques", they proposed a study of various data mining classification techniques like Decision Tree, K-Nearest Neighbour, Support Vector Machines, Naive Bayesian Classifiers, and Neural Networks. [10] They concluded that several classification techniques in datamining are available and they have their own advantages and disadvantages.…”