A Blockchain-Powered Decentralized and Secure Computing Paradigm

Thanks to the advances in machine learning, data-driven analysis tools have become valuable solutions for various applications. However, there still remain essential challenges to develop effective data-driven methods because of the need to acquire a large amount of data and to have sufficient computing power to handle the data. In many instances these challenges are addressed by relying on a cloud computing vendor. However, although commercial cloud vendors provide valuable platforms for data analytics, they can suffer from a lack of transparency, security, and privacy-preservation. Furthermore, reliance on cloud servers prevents applying big data analytics in environments where the computing power is distributed. To address these challenges, a decentralized, secure, and privacy-preserving computing paradigm is proposed to enable an asynchronized cooperative computing process amongst distributed and untrustworthy computing nodes that may have limited computing power and computing intelligence. This paradigm is designed by exploring blockchain, decentralized learning, and homomorphic encryption techniques. The performance of the proposed paradigm is evaluated via different scenarios in the simulation section.