Towards Secured Big Data Analytics in the Cloud: Two Case Studies

Securing and dealing with big data in the cloud has become a challenging issue. Best techniques, methods, and practices should be applied to ensure privacy and smooth cloud computations. Meanwhile, data analytics techniques play an important role in utilizing big data. This paper presents two different case studies that use the same datasets and perform the same processes, but they are implemented in different environments. Each case study has different scopes and aims to provide security and privacy for the data owner. Furthermore, a hybrid encryption algorithm is used to secure data using Elliptic Curve Cryptography (ECC) and Fully Homomorphic Encryption (FHE). Besides, clustering has been chosen as an instance of data analytics. Moreover, a distributed computing environment is defined to decrease the clustering time. Experiments show that the suggested distributed computing method can decrease the clustering time by up to 48% in the first case study and by up to 8.3% in the second case study.