Record linkage can enhance the utility of data by bringing data together from different sources, increasing the available information about data subjects and providing more holistic views. Doing so, however, can increase privacy risks. To mitigate these risks, a family of methods known as privacy-preserving record linkage (PPRL) was developed, using techniques such as cryptography, de-identification, and the strict separation of roles to ensure data subjects’ privacy remains protected throughout the linkage process, and the resulting linked data poses no additional privacy risks. Building privacy protections into the architecture of the system (for instance, ensuring that data flows between different parties in the system do not allow for transmission of private information) is just as important as the technology used to obfuscate private information. In this paper, we present a technology-agnostic framework for designing PPRL systems that is focused on privacy protection, defining key roles, providing a system architecture with data flows, detailing system controls, and discussing privacy evaluations that ensure the system protects privacy. We hope that the framework presented in this paper can both help elucidate how currently deployed PPRL systems protect privacy and help developers design future PPRL systems.
No takes yet. Share an insight, caveat, or question.
Nie et al. (2025) studied this question.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: