Cloud-Service-Based Blockchain Infrastructure for ML Data Incentives

In the Internet of Things (IoT) and Big Data era, large-scale data analysis necessitates rapid and efficient machine learning techniques. Traditional distributed learning methods often rely on a central server and an online privacy model, which poses challenges for scalability and data privacy. We propose a decentralized High-level Proof-of-Work (HPoW) algorithm to address these issues, leveraging blockchain technology to develop a comprehensive predictive model. Our approach ensures participant data privacy through the implementation of a differential privacy scheme and establishes a secure environment against Byzantine attacks. Furthermore, the training of numerous machine learning models in cloud computing environments can lead to data anomalies, adversely affecting resource allocation and overall system performance. We introduce a cloud computing incentive mechanism designed specifically for distributed machine learning frameworks to mitigate these challenges. We present novel methodologies and provide mathematical proof that our blockchain-based approach significantly enhances the establishment of a Nash equilibrium among participating entities. Finally, we present the machine learning service on the blockchain with the cloud service aiding through in-depth experiments to show how well it works and to conclude on a transparent Nash equilibrium value with an incentive value of 14.3 Gwei after a successful session, highlighting our proposed system’s practical viability and performance benefits.