Privacy-Preserving Federated AI for Secure Data Sharing and Regulatory Compliance in Cloud Environments

Abstract

The rapid expansion of cloud computing has enabled organizations to store, process, and analyze massive volumes of data across distributed infrastructures. However, this growth has intensified concerns regarding data privacy, regulatory compliance, and secure data sharing, particularly in domains such as healthcare, finance, and government services. Traditional centralized machine learning approaches require raw data aggregation, which exposes sensitive information to potential breaches and violates data protection regulations such as GDPR, HIPAA, and similar frameworks. Federated learning (FL) has emerged as a promising paradigm that enables collaborative model training without transferring raw data from local devices or institutional servers.

This paper explores a Privacy-Preserving Federated AI framework designed for secure data sharing and regulatory compliance in cloud environments. The proposed approach integrates federated learning with advanced privacy-preserving techniques such as differential privacy, secure multi-party computation, and homomorphic encryption to mitigate risks of data leakage and inference attacks. Additionally, it examines how compliance-aware mechanisms can be embedded within federated architectures to ensure adherence to global data protection laws.

The study highlights system design considerations, threat models, and optimization strategies to balance privacy, accuracy, and computational efficiency. It further evaluates the applicability of federated AI in real-world cloud ecosystems, emphasizing its role in enabling secure, scalable, and regulation-compliant intelligent systems.