Luc Rocher

Jan 9, 2025 | nature.com | Luc Rocher |Yves-Alexandre de Montjoye |Julien M. Hendrickx

AI techniques are increasingly being used to identify individuals both offline and online. However, quantifying their effectiveness at scale and, by extension, the risks they pose remains a significant challenge. Here, we propose a two-parameter Bayesian model for exact matching techniques and derive an analytical expression for correctness (κ), the fraction of people accurately identified in a population. We then generalize the model to forecast how κ scales from small-scale experiments to the real world, for exact, sparse, and machine learning-based robust identification techniques. Despite having only two degrees of freedom, our method closely fits 476 correctness curves and strongly outperforms curve-fitting methods and entropy-based rules of thumb. Our work provides a principled framework for forecasting the privacy risks posed by identification techniques, while also supporting independent accountability efforts for AI-based biometric systems. Advanced machine learning techniques have demonstrated the identifiability of human traces online, however, assessment of their potential risks is usually done with small-scale datasets. The authors propose a physics-based approach to evaluate the effectiveness of identification techniques from reported measurements.

Dec 10, 2024 | bmj.com | Jessica Morley |Luc Rocher

Jessica Morley, postdoctoral research associate1, Luc Rocher, lecturer2 1Digital Ethics Center, Yale University, New Haven, Connecticut, USA 2Oxford Internet Institute, Oxford, UK Correspondence to: J Morley jessica.morley{at}yale.edu Technical, rather than bureaucratic, solutions are needed Reforming the NHS by shifting from analogue to digital, from treating sickness to prevention of disease, and from hospital to community care is a priority for the UK government.1 Better use of data will...