Description

Privacy has become a considerable issue when the applications of big data are dramatically growing in cloud computing. The benefits of the implementation for these emerging technologies have improved or changed service models and improve application performances in various perspectives. However, the remarkably growing volume of data sizes has also resulted in many challenges in practice. The execution time of the data encryption is one of the serious issues during the data processing and transmissions. To ensure the confidentiality of collected data, there is a need to prevent sensitive information from reaching the wrong people by ensuring that the right people are getting it. Sensed data are always associated with different sensitivity levels based on the sensitivity the sensed data types. In this paper, we propose a Selective Encryption (SEEN) method to secure big sensing data streams that satisfies the desired multiple levels of confidentiality. This proposed method is based on two key concepts: common shared keys and selective encryption based on the type of data. Theoretical analyses and experimental results of the SEEN method show that it can significantly improve the efficiency and buffer usage at DSM without compromising the confidentiality and integrity of the data streams. Additionally the proposed system provides time based access control among the data authenticators. So that data client can access the data at specified time period which is defined by the data owner. Hence it provides effective bandwidth management when compared to our existing system.