SecureCASH: Securing Context-Aware Distributed Storage and Query Processing in Hybrid Cloud Framework

With the evolution of lightweight hand-held devices and successful deployment of wired and wireless networks for day-to-day operations, organizations are producing massive data aka big data with 4Vs: volume, variety, velocity, and veracity. Many organizations have taken advantage of their big data and developed data-driven applications for their businesses using distributed computing and storage. However, it is challenging to process such massive data in near real-time without using distributed computing such as cloud computing platforms. When organizations use public cloud platform for processing and storing their data, data must leave the organization which can invite security and privacy risks. In this paper, we investigate how organizations can take benefit of hybrid (public and private) cloud platform for distributed storage and processing for improving security and the overall performance. Further, we investigate how query process can be secured while sending data to and retrieving the data from the public cloud. Overall goal of this work is to protect sensitive data at all stages (when data is stored, traveling, processing, and retrieving) in hybrid cloud by designing secure context-aware load balancer for distributed storage and query processing (SecureCASH, for short). Performance of the proposed approach is evaluated using numerical results obtained from simulations.