A Secure Cloud Framework for Big Data Analytics Using a Hybrid Encryption Method

This paper proposes a hybrid encryption system that is based on Elliptical Curve Cryptography (ECC) and Fully Homomorphic Encryption (FHE). FHE can be used as powerful public-key encryption that allows any user to do computations on encrypted data. To develop a data analytics technique in a distributed environment, a data clustering approach is used to divide big data into several subsets. In this method, many virtual nodes process big data simultaneously and in parallel. Three datasets are used to evaluate the proposed framework. The results show that in the distributed model the execution time is decreased up to 48% using more processors working on data. Additionally, the generated results are compared with a centralized model to evaluate the efficiency and performance of the proposed framework. Experiments show that the developed framework can improve the performance of big data analytics while making sure that data is protected and secured.