In various countries and regions, sensitive citizen data —encompassing social, medical, and fiscal information— is typically dispersed among government authorities and private entities. This fragmentation poses a challenge to unlocking the full potential of combined datasets, which could yield invaluable insights across sectors such as healthcare, fraud detection, and evidence-based policymaking. To safeguard privacy, data integration —i.e., joining the data— occurs on a project-specific basis rather than on a massive scale. Each request for access to fragmented personal data initiates a separate project, where only the minimal necessary data is combined and pseudonymised. Pseudonymisation involves converting national citizen identifiers into unique codes relevant only at the project level. While today’s implementation strongly relies on trusted third parties, this approach is suboptimal due to inherent security risks and the potential for projects to become cumbersome, slow, expensive, and labour-intensive. This chapter delves into and compares two innovative solutions which exemplify privacy-by-design in the public sector, leading to significant efficiency gains in time and resources. Notably, the reliance on third party trust is minimised. These advancements offer new possibilities for more intricate research, yielding insights that positively impact healthcare, policymaking, competitiveness, and society at large.