Efficient Security Framework for Sensitive Data Sharing and Privacy Preserving on Big-Data and Cloud Platforms

Abstract

Now day's use of big data platforms is increasing for storing large amount of end user's data remotely on big data servers. Cloud computing storage was widely used for storing user's data, but cloud computing only providing the tasks of data storage but not supporting the important functionalities like computation and database operations. These operations are supported by big data systems and hence currently use of big data platform for storage in increases worldwide by enterprises. Sharing sensitive information and data resulted into big reduction in costs of enterprises for users to provide value added data and personalized services. As enterprises are sharing their important and sensitive information on big data platforms from different and many domains, it becomes necessary to provide the security and privacy in big data platform. Data security and privacy is gaining significant attentions of researchers. There are many security methods already proposed for cloud computing platform, now same methods slowly adopted on big data platform. For Big Data platforms, secure sharing of sensitive data is challenging research problem. In this paper, first we are introducing the different security and privacy preserving methods of cloud computing and big data platforms with their limitations, and then presenting the novel hybrid framework for secure sensitive data sharing and privacy preserving public auditing for shared data over big data systems including functionalities such as privacy preserving, public auditing, data security, storage, data access, deletion or secure data destruction using cloud services.