Enhancing Data Privacy in Edge Computing Through Hybrid Machine Learning and Blockchain Technologies

Abstract

The fast proliferation of edge computing has come up with serious issues of data privacy and trust within the distributed networks. The paper introduces a new hybrid system combining machine learning (ML) and blockchain platforms to provide an improved level of data privacy in edge environments. The presented approach is a hybrid of federated learning and blockchain-based secure consensus, which will allow training the models decentrally without exposing sensitive information. An encryption layer that preserves privacy guarantees the safety of the data transfer between edge nodes, whereas smart contracts handle access control and authentication independently. The hybrid infrastructure uses AI to identify anomalies and use the mitigation of threats based on their adaptability, and blockchain with a ready-to-trace immutable ledger generates transparent data. Through experimentations, it is shown that the proposed framework outperforms conventional edge privacy schemes on privacy protection, latency, and data integrity. The model obtained ~98% data privacy protection. The study adds to the coherent model that provides the connection between security, scalability and efficiency in the privacy-sensitive edge-working applications like IoT, medical, and smart cities.