Data Classification Based on Confidentiality in Virtual Cloud Environment

The aim of this study is to provide suitable security to data based on the security needs of data. It is very difficult to decide (in cloud) which data need what security and which data do not need security. However it will be easy to decide the security level for data after data classification according to their security level based on the characteristics of the data. In this study, we have proposed a data classification cloud model to solve data confidentiality issue in cloud computing environment. The data are classified into two major classes: sensitive and non-sensitive. The K-Nearest Neighbour (K-NN) classifier is used for data classification and the Rivest, Shamir and Adelman (RSA) algorithm is used to encrypt sensitive data. After implementing the proposed model, it is found that the confidentiality level of data is increased and this model is proved to be more cost and memory friendly for the users as well as for the cloud services providers. The data storage service is one of the cloud services where data servers are virtualized of all users. In a cloud server, the data are stored in two ways. First encrypt the received data and store on cloud servers. Second store data on the cloud servers without encryption. Both of these data storage methods can face data confidentiality issue, because the data have different values and characteristics that must be identified before sending to cloud severs.

[1]  R V Prasad Reddy,et al.  CLOUD DATA PROTECTION FOR THE MASSES , 2013 .

[2]  John W. Rittinghouse,et al.  Cloud Computing: Implementation, Management, and Security , 2009 .

[3]  Pradeep Singh Rawat,et al.  Quality of Service Evaluation of SaaS Modeler (Cloudlet) Running on Virtual Cloud Computing Environment using CloudSim , 2012 .

[4]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[5]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[6]  C Praveen Ram,et al.  Security as a Service (SasS): Securing user data by coprocessor and distributing the data , 2010, Trendz in Information Sciences & Computing(TISC2010).

[7]  Sunitha Abburu,et al.  An Approach for Data Storage Security in Cloud Computing , 2012 .

[8]  Donald K. Wedding,et al.  Discovering Knowledge in Data, an Introduction to Data Mining , 2005, Inf. Process. Manag..

[9]  Sunny Behal,et al.  RSA Algorithm achievement with Federal information processing Signature for Data protection in Cloud Computing , 2012, BIOINFORMATICS 2012.

[10]  Daniele Catteddu and Giles Hogben Cloud Computing. Benefits, risks and recommendations for information security , 2009 .

[11]  E. Forgy,et al.  Cluster analysis of multivariate data : efficiency versus interpretability of classifications , 1965 .

[12]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[13]  S. Archana,et al.  Survey of Classification Techniques in Data Mining , 2014 .

[14]  Belur V. Dasarathy,et al.  Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Daniele Catteddu,et al.  Cloud Computing: Benefits, Risks and Recommendations for Information Security , 2009 .

[16]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[17]  H. P. Friedman,et al.  On Some Invariant Criteria for Grouping Data , 1967 .