2025-01-09 インペリアル・カレッジ・ロンドン (ICL)
＜関連情報＞
- https://www.imperial.ac.uk/news/259794/new-mathematical-model-could-ensure-safer/
- https://www.nature.com/articles/s41467-024-55296-6
識別技術の有効性をモデル化するスケーリング則 A scaling law to model the effectiveness of identification techniques
Luc Rocher,Julien M. Hendrickx & Yves-Alexandre de Montjoye
Nature Communications Published:09 January 2025
DOI:https://doi.org/10.1038/s41467-024-55296-6
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.
