Today people leave digital breadcrumbs wherever they go and whatever they do online and offline. This data
dramatically increases our capacity to understand and affect the behavior of individuals and collectives, has
been key to recent advances in AI, but also raises fundamentally new privacy and fairness questions. The
Computational Privacy Group aims to provide leadership, in the UK and beyond, in the safe, anonymous, and
ethical use of large-scale behavioral datasets coming from the Internet of Things (IoT) devices, mobile
phones, credit cards, browsers, etc.
Our projects have already demonstrated the limits of data anonymization (or de-identification) in effectively protecting the privacy of individuals in Big Data, the risk of inference in behavioral datasets coming from mobile phone, and developed solutions to allow individuals and companies to share data safely. While technical in nature, our work has had significant public policy implication for instance in reports of United Nations, FTC, and the European Commission as well as in briefs to the U.S. Supreme Court.
We develop statistical and machine learning techniques to uniquely identify individuals in large-scale behavioral datasets. These techniques show the limits of pseudonymization and anonymization in protecting people's privacy.
Modern privacy is not only about controlling the information but also the ability to control how this information is used e.g. for insurance pricing or ad-targeting. We study fairness in algorithmic-decision making and, more generally, the impact of AI on society.
Yves-Alexandre is organizing a panel in Davos on ‘Europe’s digital leadership: can AI and privacy co-exist?’
In a new paper published in Nature Communications, Luc and Yves-Alexandre show how the incompleteness of datasets does not provide plausible deniability to participants. Contradicting previous claims, they show that sampling does not decrease the risk of re-identification.
On 17 June 2019, Andrea Gadotti presented CPG’s research at Westminster as part of the "In conversation with the National Statistician" event. On 26 June, he presented at the Evidence Week, organised by Sense About Science.
Thibaut received his PhD in computational statistics from Oxford. Thibaut's research interests include the application of machine learning techniques on behavioral datasets for identification learning as well as adversarial machine learning.
Ana-Maria graduated from Ecole Polytechnique, France and holds an MSc in Computer Science from EPFL, Switzerland. Her research interests includes machine learning for re-identification.
Ali has a MSc in high energy physics jointly from the University of Southampton and CERN. His research focuses on quantifying the privacy of human behavioral datasets, as well as studying the impact of AI algorithmic decision making on society. View website.
Andrea received his MSc in mathematical logic from the University of Turin. His research interests include differential privacy, determining vulnerabilities in data-release systems, and designing privacy-preserving mechanisms.
Florimond received his MSc in applied mathematics from UCLouvain. His research interests include obfuscation and privacy risks in Web search data .
Arnaud has masters in mathematics from Paris VI, Paris XI and from Ecole Centrale Paris. His research interests include biometrics and profiling.
Assistant (if urgent): Fay Miller, +44 20 7594 8612
We are located at the Data Science Institute in the William Penney Laboratory. The best entry point is via Exhibition road, through the Business school (see map below). From there, just take the stairs towards the outdoor court. Enter the outdoor corridor after the court and the institute will be on your right (please press the Data Science intercom button for access).
Please address mails to:
Department of Computing
Imperial College London
180 Queens Gate
London SW7 2AZ