Duncan Smith

Duncan Smith is a researcher specializing in statistical disclosure control (SDC) and privacy-preserving techniques. His work focuses on developing algorithms and software to protect data privacy while maintaining statistical utility, particularly in contexts involving linked or synthesized datasets. He holds a PhD in Statistical Disclosure Control from an institution where he was supervised by Michael Elliot and Ian Buchan.

Research interests include Bayesian methods, highly structured stochastic systems, and computational privacy solutions. His recent work explores re-identification risks in data linkage, secure pseudonymisation for probabilistic record linkage, and unified risk assessment frameworks for data disclosure.

He has contributed to projects such as the 2019–2022 'Statistical Modelling' initiative addressing migration data analysis. His research aligns with UN Sustainable Development Goals, particularly Digital Futures. He has peer-reviewed for journals like Journal of Official Statistics and International Statistical Review.

No scientific awards are listed. His activities include supervising his own PhD work and collaborating on privacy practices with data stewardship organizations.