文章基本信息

标题：A Secure Data Classification Model in Cloud Computing Using Machine Learning Approach
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作者：Kulwinder Kaur ; Vikas Zandu
期刊名称：International Journal of Grid and Distributed Computing
印刷版ISSN：2005-4262
出版年度：2016
卷号：9
期号：8
页码：13-22
出版社：SERSC
摘要：Cloud computing offers numerous benefits including scalability, availability and many services. But with its wide acceptance all over the globe, new risks and vulnerabilities have appeared too. Cloud computing provides facility of storing and accessing information and programs over the web without bothering the storage space on system. Storing the data on cloud eliminates one’s worries about space considerations, buying new storage equipment or managing their data, rather they are able to access their data any time from any place provided they have internet access. But the rising security problems have resisted the organizations from connecting with cloud computing completely. Hence security risks have appeared as the main disadvantage of cloud computing. This paper involves the efforts to analyze the security issues and then proposes a framework to address these security issues at the authentication and storage level in cloud computing. While addressing the security issues the first and the foremost thing is to classify what data needs security and what data needn't bother with security and hence data gets classified into two classes sensitive and non-sensitive. To achieve data classification, a data classification approach based on the confidentiality of data is proposed in this paper. Following that an efficient security mechanism has to be deployed by means of encryption, authentication, and authorization or by some other method to ensure the privacy of consumer’s data on cloud storage.
关键词：Cloud Computing; security issues; privacy preserving; Integrity; ;confidentiality; availability; graphical passwords