Yves-Alexandre de Montjoye – Chuàngkàn Bālí

AUTHOR

: Lecturer at Data Science Institute, Imperial College London; A Belgian citizen, Yves-Alexandre de Montjoye is a lecturer (eq. Assistant Professor) at Imperial College London's new Data Science Institute (joint with the Department of Computing). He was previously a postdoctoral researcher at Harvard IQSS working with Prof. Latanya Sweeney and Prof. Gary King and he received my PhD from MIT under the supervision of Prof. Alex "Sandy" Pentland (250-words bio).
His research aims at understanding how the unicity of human behavior impacts the privacy of individuals in large-scale metadata datasets. His work has been covered in The New York Times, BBC News, CNN, Wall Street Journal, Harvard Business Review, Le Monde, Die Spiegel, Die Zeit, El Pais, and in reports of the World Economic Forum, United Nations, OECD, FTC, and the European Commission, as well as in my talks at TEDxLLN and TEDxULg. He recently wrote a white paper for Brookings on the use and privacy metadata as well as op-eds for the World Economic Forum, Christian Science Monitor, and Le Monde. He worked for the Boston Consulting Group and acted as an expert for the Bill and Melinda Gates Foundation and the United Nations. He is a fellow of the ID³ Foundation, the B.A.E.F. Foundation, and a research associate at Data-Pop. He is organizing NetMob, the International Conference on the Analysis of Mobile Phone Datasets, and he isserving on numerous program committee.
He was recently named an Innovator under 35 for Belgium (TR35).

Articles from this author

Society / 2017-11-23

Using big data while respecting privacy: new prospects

Yves-Alexandre de Montjoye / Lecturer at Data Science Institute, Imperial College London

The first real anonymization algorithms were developed in the mid-1990s. These methods worked very well over the next ten to fifteen years, until the beginning of a new era, that of big data. Everybody generates data from which they can be easily re-identified. The era of big data leaves no room for anonymization. The offered solution reverses the problem: instead of sharing an anonymized database, data will be stored on a secure infrastructure with a controlled access. Those who wish to use these data cannot access it directly. Instead, they will download an algorithm, which we will check and run before authorizing it to search the database for the pieces of required data. This secure infrastructure will allow researchers, statistical institutes or companies to use the potential of big data while offering very strong guarantees in terms of protection of user privacy.