A scaling law to model the effectiveness of identification techniques
(2025) Nature Communications — Vol. 16, n° 1 (2025)
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- Authors
Rocher, Luc 
- Abstract
- 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.
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Citations
Rocher, L., Hendrickx, J., & Montjoye, Y.-A. d. (2025). A scaling law to model the effectiveness of identification techniques. Nature Communications, 16(1). https://doi.org/10.1038/s41467-024-55296-6 (Original work published 2025)