  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 dominant cloud computing vendor, but, although commercial cloud vendors
provide valuable platforms for data analytics, they can suffer from a lack of
transparency, security, and privacy-perservation. Furthermore, reliance on
cloud servers prevents applying big data analytics in environments where the
computing power is scattered. To address these challenges, a decentralize,
secure, and privacy-preserving computing paradigm is proposed to enable an
asynchronized cooperative computing process amongst scattered and untrustworthy
computing nodes that may have limited computing power and computing
intelligence. This paradigm is designed by exploring blockchain, decentralized
learning, homomorphic encryption, and software defined networking(SDN)
techniques. The performance of the proposed paradigm is evaluated via different
scenarios in the simulation section.
