Modeling Data Sovereignty in Public Cloud—A Comparison of Existing Solutions

Abstract

Data sovereignty has emerged as a critical concern for enterprises, cloud service providers (hyperscalers), end-users, and policymakers at both national and international levels. In response, cloud-based distributed computing models have been proposed as frameworks to enforce data sovereignty requirements. This study aims to evaluate and enhance data sovereignty practices within public cloud environments. Through a comprehensive literature review, we analyze existing reference architectures and solutions that address data sovereignty, identifying the technological and economic constraints they impose, such as increased computational costs associated with specific frameworks and cryptographic measures. To address these challenges, we propose an abstract data sovereignty model designed to aid system designers and architects in developing compliant cloud-based systems. Additionally, we conduct computational experiments assessing the performance of the IDS connector, a key data sovereignty tool, deployed on the Google Cloud Platform and Microsoft Azure. Results reveal that while the geographic location of the software significantly impacts performance, the choice of cloud platform minimally influences the IDS connector’s efficiency. These findings offer insights into optimizing data sovereignty strategies for cloud solutions, with implications for future system design and policy development.