Personal Data Management System Architectures: A Systematic Review

The increase in personal data generation and the evolving landscape of digital information management necessitate robust and secure software architectures for personal data management systems. This systematic literature review examines the architectures proposed for such systems, classifying them as centralized, decentralized, or hybrid. The classification criterion is applied to the evidence the studies report rather than to the label its authors adopt. It separates the custody of the data from the coordination of its processing, because two systems that describe themselves as decentralized distribute only the former. The review analyzes 15 studies, retrieved from three databases, one search engine, and other sources, published between January 2014 and February 2026, examining the methodologies, technologies, and architectural strategies employed in personal data management systems. The review highlights the strengths and limitations of different software architectures in terms of scalability, security, and compliance with the European Union’s General Data Protection Regulation, a regulatory regime addressed explicitly by several of the reviewed architecture studies. Key findings reveal a significant gap in the current literature. No shared benchmark or dataset appears in the corpus. The evidence therefore supports qualitative but not empirical comparison between architectural approaches.