A Framework for Privacy-Preserving Machine Learning in Sovereign Cloud Environments

Abstract

The rapid growth of machine learning (ML) technologies has raised concerns about the privacy and security of sensitive data used in training models. Privacy-preserving techniques such as federated learning, homomorphic encryption, and differential privacy are emerging solutions to protect data in ML applications. However, these techniques often face challenges in terms of scalability, performance, and compliance with data privacy regulations. Sovereign Cloud environments, characterized by strict data governance and jurisdictional controls, offer a potential solution for addressing these challenges. This paper presents a novel framework for integrating privacy-preserving ML techniques within Sovereign Cloud infrastructures. By combining cutting-edge cryptographic approaches with the data sovereignty features of Sovereign Clouds, our framework ensures data privacy, legal compliance, and efficient machine learning at scale. We discuss key challenges in data privacy, scalability, and legal compliance, and propose a set of best practices for deploying privacy-preserving ML in these environments. Additionally, we evaluate the proposed framework through case studies, demonstrating its potential in sectors such as healthcare and finance. The results show that our framework provides a balanced approach to privacy, scalability, and performance, contributing to the future of secure and responsible ML deployment.