UNDER EMBARGO UNTIL 10 AM GMT / 5 AM ET THURSDAY 9 JANUARY 2025 

Pioneering new mathematical model could help protect privacy and ensure safer use of AI   

AI tools are increasingly being used to track and monitor us both online and in-person, yet their effectiveness comes with big risks. Computer scientists at the Oxford Internet Institute, Imperial College London, and UCLouvain have developed a new mathematical model which could help people better understand the risks posed by AI and assist regulators in protecting peoples’ privacy. The findings have been published today (9 January) in Nature Communications. 

For the first time, the method provides a robust scientific framework for evaluating identification techniques, especially when dealing with large-scale data. This could include, for instance, monitoring how accurate advertising code and invisible trackers are at identifying online users from small pieces of information such as time zone or browser settings (a technique called ‘browser fingerprinting’). 

Lead author Dr Luc Rocher, Senior Research Fellow, Oxford Internet Institute, part of the University of Oxford, said: “We see our method as a new approach to help assess the risk of re-identification in data release, but also to evaluate modern identification techniques in critical, high-risk environments. In places like hospitals, humanitarian aid delivery, or border control, the stakes are incredibly high, and the need for accurate, reliable identification is paramount.” 

The method draws on the field of Bayesian statistics to learn how identifiable individuals are on a small scale, and extrapolate the accuracy of identification to larger populations up to 10x better than previous heuristics and rules of thumb. This gives the method unique power in assessing how different data identification techniques will perform at scale, in different applications and behavioural settings. This could help explain why some AI identification techniques perform highly accurately when tested in small case studies but then misidentify people in real-world conditions.  

The findings are highly timely, given the challenges posed to anonymity and privacy caused by the rapid rise of AI-based identification techniques. For instance, AI tools are being trialled to automatically identify humans from their voice in online banking, their eyes in humanitarian aid delivery, or their face in law enforcement.

According to the researchers, the new method could help organisations to strike a better balance between the benefits of AI technologies and the need to protect people's personal information, making daily interactions with technology safer and more secure. Their testing method allows for the identification of potential weaknesses and areas for improvement before full-scale implementation, which is essential for maintaining safety and accuracy. 

Co-author Associate Professor Yves-Alexandre de Montjoye (Data Science Institute, Imperial College, London) said: “Our new scaling law provides, for the first time, a principled mathematical model to evaluate how identification techniques will perform at scale. Understanding the scalability of identification is essential to evaluate the risks posed by these re-identification techniques, including to ensure compliance with modern data protection legislations worldwide.”  

Dr Luc Rocher concluded: “We believe that this work forms a crucial step towards the development of principled methods to evaluate the risks posed by ever more advanced AI techniques and the nature of identifiability in human traces online. We expect that this work will be of great help to researchers, data protection officers, ethics committees, and other practitioners aiming to find a balance between sharing data for research and protecting the privacy of patients, participants, and citizens.”  

Notes to editors: 

For media inquiries and interview requests, contact: press@oii.ox.ac.uk

The study ‘A scaling law to model the effectiveness of identification techniques’ will be published in Nature Communications at 10 am GMT / 5 am ET on Thursday 9 January 2025 at https://www.nature.com/articles/s41467-024-55296-6 

To view a copy of the paper before this, under embargo, contact: press@oii.ox.ac.uk

About the Research   

The work by was supported by a grant awarded by to Luc Rocher by Royal Society Research Grant RG\R2\232035, the John Fell OUP Research Fund, the UKRI Future Leaders Fellowship [grant MR/Y015711/1], and by the F.R.S.-FNRS. Yves -Alexandre de Montjoye acknowledges funding from the Information Commissioner Office. The funders which had no role in the study design, data collection and analysis, decision to publish, or preparation of the article.  

About the Oxford Internet Institute (OII)   

The Oxford Internet Institute (OII) is a multidisciplinary research and teaching department of the University of Oxford, dedicated to the social science of the Internet. Drawing from many different disciplines, the OII works to understand how individual and collective behaviour online shapes our social, economic and political world. Since its founding in 2001, research from the OII has had a significant impact on policy debate, formulation and implementation around the globe, as well as a secondary impact on people’s wellbeing, safety and understanding. Drawing on many different disciplines, the OII takes a combined approach to tackling society’s big questions, with the aim of positively shaping the development of the digital world for the public good. https://www.oii.ox.ac.uk/   

About the University of Oxford  

Oxford University has been placed number 1 in the Times Higher Education World University Rankings for the ninth year running, and ​number 3 in the QS World Rankings 2024. At the heart of this success are the twin-pillars of our ground-breaking research and innovation and our distinctive educational offer. Oxford is world-famous for research and teaching excellence and home to some of the most talented people from across the globe. Our work helps the lives of millions, solving real-world problems through a huge network of partnerships and collaborations. The breadth and interdisciplinary nature of our research alongside our personalised approach to teaching sparks imaginative and inventive insights and solutions. 

Through its research commercialisation arm, Oxford University Innovation, Oxford is the highest university patent filer in the UK and is ranked first in the UK for university spinouts, having created more than 300 new companies since 1988. Over a third of these companies have been created in the past five years. The university is a catalyst for prosperity in Oxfordshire and the United Kingdom, contributing £15.7 billion to the UK economy in 2018/19, and supports more than 28,000 full time jobs.