In times of big data, traditional techniques for data protection no longer suffice: even if identifiable information is left out, such as phone number or name, and even if noise is added, re-identification is still possible. Prof. de Montjoye of Imperial College London describes the problems, the possible solutions and the most recent developments.
We live in a time when information about most of our movements and actions is collected and stored in real-time. The availability of large-scale behavioral data dramatically increases our capacity to understand and potentially affect the behavior of individuals and collectives.
The use of this data, however, raises legitimate privacy concerns. Anonymization is meant to address these concerns: allowing data to be fully used while preserving individuals’ privacy. In this talk, Prof. de Montjoye will first discuss how traditional data protection mechanisms fail to protect people’s privacy in the age of big data. More specifically, he will show how the mere absence of obvious identifiers such as name or phone number or the addition of noise are not enough to prevent re-identification. Second, de Montjoye will describe what he sees as a necessary evolution of the notion of data anonymization towards an anonymous use of data. He will then conclude by discussing some of the modern privacy engineering techniques currently developed to allow large-scale behavioral data to be used while giving individual strong privacy guarantees.
Yves-Alexandre de Montjoye
Yves-Alexandre de Montjoye is Associate Professor at Imperial College London. Currently he is a Special Adviser on AI and Data Protection to European Commission Justice Commissioner Reynders and a Parliament-appointed expert to the Belgian Data Protection Agency. In 2018-2019, he was a Special Adviser to European Commissioner Vestager for who he co-authored the Competition policy for the digital era report.
His research has been published in Science and Nature Communications and has received a lot of media attention (BBC, CNN, New York Times, Wall Street Journal, Harvard Business Review, etc.). His work on the shortcomings of anonymization has appeared in reports of the World Economic Forum, the FTC, the European Commission, and the OECD. Yves-Alexandre worked for the Boston Consulting Group and acted as an expert for both the Bill and Melinda Gates Foundation and the United Nations. He obtained his Ph.D. from MIT in 2015 and obtained, over a 6-year period, an M.Sc. from UCLouvain in Applied Mathematics, an M.Sc. (Centralien) from École Centrale Paris, an M.Sc. from KU Leuven in Mathematical Engineering, as well as his B.Sc. in engineering from UCLouvain.
- 30 June 2021, 14-15h
- Location: online
- Contact: Laura Alonso
- Language: English
- Target audience: Ph.D.-students with knowledge of the technical aspects of AI & machine learning
- Registration: until 29 June 2021
- Prerequisites: M.Sc. degree
- Price, free, but registration is required
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Sense & Sensibility of AI
Seminar series on AI Ethics:
Fairness, Privacy, Trustworthiness
AI has an increasing influence on our daily lives, examples include automated decision-making for high-stake decisions such as mortgages and loans, automated risk assessments for bail or recommenders on the internet. These AI systems carry the risk of creating filter bubbles and polarization. While AI is being rolled out into society, the discussion on how AI-based systems may align with and even affect our values, is pushed to the forefront. We gave the computer senses, but how can we give it sensibility? It requires a multi-disciplinary view, where both technical and non-technical perspectives have a prominent place.
In our lecture series ‘Sense & Sensibility of AI,’ we aim for Ph.D. Students to learn about the different aspects of Ethics in AI, not only to become aware of them but also to learn about the impact of AI on society and about methodologies to identify, assess, and possibly address ethical issues. The monthly seminars tackle subjects such as bias and fairness, privacy, trustworthiness, balancing technical, social, and regulatory perspectives.
The series is targeted towards doctoral students working in the broad field of AI and data science. To understand the lectures in full, it may be required to have a background in the technical aspects of AI/machine learning.
Sense & Sensibility of AI is a seminar series developed by Flemish AI Academy in collaboration and with the support of all our partners, all universities in Flanders, and Knowledge Center Data & Society.