K-NN classifier for data confidentiality in cloud computing

Zardari, M.A. and Jung, L.T. and Zakaria, N. (2014) K-NN classifier for data confidentiality in cloud computing. In: UNSPECIFIED.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Securing the data in cloud is still a challenging issue. In cloud, many techniques are used to secure the data. Data encryption is a data security technique which is widely used for data security. Deciding data security approach for the data without understanding the security needs of the data is not a technically valid approach. Before applying any security on data in cloud, it is best to know the security needs of the data. What data need security and what data do not need security. In this paper, we propose a data classification approach based on data confidentiality. K-NN data classification technique is modulated in the cloud virtual environment. The aim to use K-NN is to classify the data based on their security needs. The data is classified into two classes, sensitive and non-sensitive (public) data. After data classification we got that what data need security and what data does not need security. The RSA algorithm is used to encrypt the sensitive data to keep it secure. We used CloudSim simulator to find the results of proposed approach in cloud. The proposed approach will easily decide the security needs of the data. After the data classification, it is easy to select an appropriate security for data according to the need of data. The results show that this approach is appropriate as compared to store data in cloud without understanding the security requirements of data. © 2014 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Impact Factor: cited By 16
Uncontrolled Keywords: Cloud computing; Cryptography; Distributed computer systems; Security of data, Data classification; Data confidentiality; Data encryption; k-NN classifier; Nonsensitive; Security approach; Security requirements; Sensitive datas, Classification (of information)
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 09:02
Last Modified: 25 Mar 2022 09:02
URI: http://scholars.utp.edu.my/id/eprint/31201

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