Using solid in secure data platforms to support data sovereignty and privacy preserving computation of personal data

Abstract

Privacy-preserving data analysis must carefully balance the need for secure, meaningful computation on sensitive personal data with the fundamental rights of individuals to retain control over their information. Solid (Social Linked Data) presents an open protocol where users store and manage their data in personal, access-controlled pods. However, its potential for integration as a decentralized data store into existing infrastructures for privacy-preserving computations remains underexplored. This thesis addresses the question of how Solid can be effectively integrated into such platforms to support decentralized data sharing while meeting the technical requirements of privacy-aware research. To address this, we propose the Solid Gateway, a mediator that facilitates consent-driven access to Solid Pods within existing analysis environments. The Solid Gateway introduces request-specific authentication and authorization, manages access permissions, and orchestrates the retrieval of only the data necessary to fulfill individual data requests. Central to this approach is a novel granular data-sharing strategy, which restructures user data into minimal request-specific subsets, thus reducing unnecessary data transfers and limiting the exposure of irrelevant information. This ensures that contributors retain sovereignty over their data while allowing privacy-preserving analysis to operate on decentralized sources. Our experimental evaluation, conducted on controlled artificial datasets, confirms the feasibility of our integration. The results demonstrate a significant reduction in data exposure while achieving improved data retrieval performance compared to existing approaches. Also, we compare our proposed solution against the WellFort architecture and demonstrate that our approach offers competitive fetch performance and significantly improves processing efficiency. Although the controlled nature of the evaluation limits comparability with existing platforms, it provides a reproducible foundation for future studies and practical deployments. This work contributes a concrete, extensible design for combining Solid with privacy-preserving computation, identifies key trade-offs between privacy, performance, and system complexity, and opens pathways for future research into SPARQL integration, validation with established datasets, and the application of FAIR principles within Solid.