1 research outputs found
Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing
For the modern world where data is becoming one of the most valuable assets,
robust data privacy policies rooted in the fundamental infrastructure of
networks and applications are becoming an even bigger necessity to secure
sensitive user data. In due course with the ever-evolving nature of newer
statistical techniques infringing user privacy, machine learning models with
algorithms built with respect for user privacy can offer a dynamically adaptive
solution to preserve user privacy against the exponentially increasing
multidimensional relationships that datasets create. Using these privacy aware
ML Models at the core of a Federated Learning Ecosystem can enable the entire
network to learn from data in a decentralized manner. By harnessing the
ever-increasing computational power of mobile devices, increasing network
reliability and IoT devices revolutionizing the smart devices industry, and
combining it with a secure and scalable, global learning session backed by a
blockchain network with the ability to ensure on-device privacy, we allow any
Internet enabled device to participate and contribute data to a global privacy
preserving, data sharing network with blockchain technology even allowing the
network to reward quality work. This network architecture can also be built on
top of existing blockchain networks like Ethereum and Hyperledger, this lets
even small startups build enterprise ready decentralized solutions allowing
anyone to learn from data across different departments of a company, all the
way to thousands of devices participating in a global synchronized learning
network.Comment: 9 pages, 8 figure