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.
While governments are ramping up their efforts to slow down the spread of COVID-19, contact tracing apps are being developed to record interactions and warn users if one of their contacts is later diagnosed positive. These apps could help avoid long-term confinement, but also …
Used correctly, mobile phone data could help monitor the effectiveness of lockdown measures and track contacts of people who have been tested positive. We've been asked if the data could be collected and used effectively without enabling mass surveillance. This is our response.
Yves-Alexandre is organizing a panel in Davos on ‘Europe’s digital leadership: can AI and privacy co-exist?’
Yves-Alexandre is an Associate Professor at Imperial College London. He received his PhD from MIT before joining Harvard IQSS for his postdoc. He currently is a Special Adviser on AI and Data Protection to EC Justice Commissioner Reynders and a Parliament-appointed expert to the Belgian Data Protection Agency (APD-GBA).
Luc is a postdoctoral researcher studying the limits of privacy in the modern age, and received a PhD in Applied mathematics from UCLouvain. Luc's work has challenged the technical and legal adequacy of current de-identification techniques to anonymise data.
Originally from Romania, Ana-Maria has a background in mathematics and computer science. Her research focuses on new machine learning-based privacy attacks in large-scale behavioral datasets.
Ali has an MSc in High Energy Particle Physics jointly from the University of Southampton and CERN. His research focuses on quantifying the privacy of human behavioral datasets and the societal impact of AI algorithmic decision.
Andrea received a BSc in math and a MSc in mathematical logic from the University of Turin. His research interests include differential privacy, privacy attacks against systems processing personal data, and the design of privacy-preserving mechanisms.
Originally from Belgium, Florimond has an MSc in applied mathematics. His research interests include differential privacy, obfuscation, and privacy leaks in networks.
Originally from India, Shubham received a BTech in computer science and engineering from IIT Bombay. His research interests include fairness in machine learning systems, scalable privacy-preserving systems, and network security.
Originally from France, Arnaud has masters in stochastics from Paris VI, fundamental mathematics from Paris XI, and a diplôme d'ingénieur from Ecole Centrale Paris. His research interests include reinforcement learning, 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