Enhanced Privacy-Preserving Cloud Big Data Analytics with Decentralized SOLID Personal Online Datastores

While a plethora of Privacy-Preserving Cloud Big Data Analytics models and paradigms have been proposed in literature, computational overheads still remain an important limitation at today. With the goal of addressing this relevant gap, in this paper we propose an innovative framework for supporting privacy-preserving Cloud big data analytics via the application of decentralized SOLID Personal Online Datastores (PODs), by specifically considering the case of multiple-Cloud environments. Indeed, PODs ensure the privacy of user data across multiple, different (networked) systems. The final aim is that of reducing the execution of computationally-expensive anonymization algorithms at the node, while improving throughput and scalability of the target system. A comprehensive experimental assessment and analysis session confirms the benefits coming from our research.