The goal of this project is to study how users can defend their privacy against profiling attempts from different service providers like search engines, online social networks, etc, by obfuscating their own data. In particular, the research focuses on the investigation of techniques, like differential privacy, based on the idea to let the user artificially decrease the signal-to-noise ratio of the information he/she discloses.
Principal Investigator
Giovanni Neglia, Inria
Project's partner(s)
Charles Bouveyron, LJAD
Michela Chessa, GREDEG
Bruno Ribeiro, Deparment of Computer Science, Purdue University, Indiana, USA
Chuan Xu, Giovanni Neglia. What else is leaked when eavesdropping Federated Learning?. CCS workshop Privacy Preserving Machine Learning (PPML), Nov 2021, Online. ⟨10.1145/1122445.1122456⟩. ⟨hal-03364766v2⟩
Oualid Zari, Chuan Xu, and Giovanni Neglia. “Efficient passive membership inference attack in federated learning”. In: NeurIPS workshop on Privacy in Machine Learning (PriML). Virtual., Dec. 2021. https://hal.archives-ouvertes.fr/hal-03410152/
Related Documentation
Project MYDATA presentation (Day of projects restitution of the Academy “Networks, Information and Digital Society", 14/03/2022)
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