Computational Privacy Group

We are a young research group at Imperial College London studying the privacy risks arising from large scale behavioral datasets. We develop attack models and design solutions to collect and use data safely.

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.

Research Areas


Identification Learning

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.

Safe Data Sharing

We build privacy-preserving and conscientious techniques to collect and use data while respecting people's privacy. For instance, we're building with MIT the OPAL (Open Algorithms) platform to safely share location data and openPDS to give individuals control over their data.

Societal impact of AI

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.

News and Events


Selected publications


The full list of our papers is available on Google Scholar.

Our Team


Yves-Alexandre de Montjoye

Group leader

Yves-Alexandre is an Assistant Professor at Imperial College London. He received his PhD from the MIT Media Lab and was a Postdoc at Harvard. He holds MScs in Applied Math from Louvain, Ecole Centrale Paris (Centralien), and KULeuven. View website - View bio

Thibaut
Lienart

Postdoc

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.

Luc
Rocher

Postdoc

Luc received a PhD in applied mathematics from UCLouvain. Luc studies the limits of privacy and anonymity in the modern age, challening the technical and legal adequacy of current de-identification techniques. View website

Stefano
Marrone

Visiting PhD student

Stefano is a Ph.D. fellow at the University of Naples Federico II and a visiting fellow in the Computational Privacy Group. His research interests include pattern recognition, adversarial learning and embedded systems. View website

Contact


Email:X@Y where X=demontjoye, Y=imperial.ac.uk.
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