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Section: Partnerships and Cooperations

National Initiatives

FUI

HuMa

  • Title: HuMa.

  • Type: FUI.

  • Duration: Juin 2015 - Mai 2018.

  • Coordinator: INTRINSEC.

  • Others partners: Inria, SYDO, Wallix, INSA Lyon, CASSIDIAN Cybersecurity, Oberthur, INTRINSEC.

  • Abstract:

    The goal of huMa is to improve the tools used to distinguish legitimate network flows from attacks in complex systems including IoT.

ANR

BIOPRIV

  • Title: Application of privacy by design to biometric access control.

  • Type: ANR.

  • Duration: April 2013 - March 2017.

  • Coordinator: Morpho (France).

  • Others partners: Morpho (France), Inria (France), Trusted Labs (France).

  • See also: http://planete.inrialpes.fr/biopriv/.

  • Abstract: The objective of BIOPRIV is the definition of a framework for privacy by design suitable for the use of biometric technologies. The case study of the project is biometric access control. The project will follow a multidisciplinary approach considering the theoretical and technical aspects of privacy by design but also the legal framework for the use of biometrics and the evaluation of the privacy of the solutions.

Inria Project Labs

CAPPRIS

  • Title: CAPPRIS

  • Type: Inria Project Lab

  • Duration: January 2011 - 2016.

  • Coordinator: PRIVATICS

  • Others partners: Inria (CIDRE, Comete, Secsi,Smis), Eurecom, LAAS and CRIDS

  • Abstract: Cappris (Collaborative Action on the Protection of Privacy Rights in the Information Society) is an Inria Project Lab initiated in 2013. The general goal of Cappris is to foster the collaboration between research groups involved in privacy in France and the interaction between the computer science, law and social sciences communities in this area.

Inria CNIL project

MOBILITICS

  • Title: MOBILITICS

  • Type: joint project.

  • Duration: January 2012 - Ongoing.

  • Coordinator: CNIL.

  • Others partners: CNIL.

  • Abstract: Platform for mobile devices privacy evaluation. This project strives to deploy an experimental mobile platform for studying and analyzing the weaknesses of current online (smartphone) applications and operating systems and the privacy implications for end-users. For instance, one of the objectives is to understand trends and patterns collected when they are aimed at obtaining general knowledge that does not pertain to any specific individual. Examples of such tasks include learning of commuting patterns, inference of recommendation rules, and creation of advertising segments.



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