Privacy-Preserving Machine Learning and Secure Data Sharing for Big Data Analytics

Abstract

Big data analytics has become central to digital health, smart cities, industrial internet of things, and financial services. Traditional data pipelines move raw records into central repositories where machine learning models are trained and deployed. This approach increases the risk of privacy violations, regulatory non compliance, and security breaches. At the same time, many learning tasks need data from several organizations or devices that cannot share records directly. This article presents a privacy preserving machine learning framework that combines federated learning, secure aggregation, and risk aware data sharing policies. The framework supports heterogeneous data sources and can be deployed on mobile devices, medical cyber physical systems, and smart building controllers. A practical design is proposed, together with analytical models of privacy loss, communication cost, and model utility. Experimental results on synthetic and real world inspired workloads show that the framework can keep useful predictive performance while reducing raw data transfer and exposure of sensitive attributes. The study offers design guidelines for engineers who build privacy preserving analytics in real deployments.