David Pointcheval - Privacy-preserving aggregation of data from multiple sources

14:00
Jeudi
10
Jan
2019
Organisé par : 
L'équipe Keynote du LIG : Nicolas Peltier, Renaud Lachaize, Dominique Vaufreydaz
Intervenant : 
David Pointcheval (CNRS, ENS)
Équipes : 

Information détaillée : 

 

David Pointcheval obtained his PhD in Computer Science from the University of Caen in 1996.
Since 1998, he has been a CNRS researcher, in the Computer Science Department at Ecole Normale Supérieure, Paris, France, in the Cryptography Team, that he has been leading since 2005. This team has also been associated to Inria since 2008.
Since 2017, he has been the chair of the Computer Science Department at ENS.
His research focuses on provable security of cryptographic primitives and protocols.
He is an author of more than 150 international conference and journal papers, and an inventor of a dozen of patents.
He has been one of the nine elected directors in the board of IACR, for 9 years.
He has been program chair for several international conferences in cryptography, including PKC 2010 and Eurocrypt 2012.
He has recently been awarded an ERC Advanced Grant, from the European Commission, on the Privacy for the Cloud.

 

Résumé : 

Gigabits of data are regularly aggregated in order to deliver statistics and recommendations, or even to make decisions. These data are processed in clear by many providers that offer valuable services, but at the cost of a huge risk with respect to privacy. The providers themselves or even hackers could exploit these data for malicious purposes. Privacy-by-design would be preferable.
Cryptography has recently developed new tools in order to allow aggregation on encrypted data, with fully homomorphic encryption and functional encryption. However, whereas they work well for one user, they fail to aggregate data that come from different sources, in particular when these sources do not trust each other.
In this talk, we will present new techniques of aggregation for data that come from multiple mutually distrustful sources, so that privacy is guaranteed, and the data owners keep control on the performed aggregation.

This is joint work with Jérémy Chotard, Edouard Dufour Sans, Romain Gay, and Duong Hieu Phan.