Privacy-Aware Adaptive Data Encryption Strategy of Big Data in Cloud Computing

Privacy issues have become a considerable issue while the applications of big data are growing dramatically fast in cloud computing. The benefits us implementing 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 time execution of encrypting data is one of the serious issues during the processes of data processing and transmissions. Many current applications abandon data encryptions in order to reach an adoptive performance level, companions with privacy concerns. In this paper, we concentrate on privacy issue and propose a novel data encryption approach, named as Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data using privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements. The performance of D2ES has been evaluated in our experiments, which provides the proof of the privacy enhancement.