Every year Inria International Relations Department has a few postdoctoral positions in order to support Inria international collaborations.
The postdoctoral contract will have a duration of 12 to 24 months. The default start date is November 1st, 2026 and not later than January, 1st 2027. The postdoctoral fellow will be recruited by one of the Inria Centres in France but it is recommended that the time is shared between France and the partner’s country (please note that the postdoctoral fellow has to start his/her contract being in France and that the visits have to respect Inria rules for missions).
This postdoctoral position is proposed within the TRiBE research team at Inria Saclay and focuses on advancing privacy-preserving mobility analytics through interpretable behavioral modeling.
Mobility is a defining human behavior and an increasingly traceable signal in today’s digital ecosystem. It underpins data-driven decision-making and fuels the expanding mobile services market (projected to reach $463 billion by 2032). However, the exploitation of mobility data raises significant privacy concerns. Persistent routines and timedependent social behaviors make individual mobility traces highly distinctive, increasing the risk of privacy exposure. While existing privacy-preserving techniques reduce re-identification risks, they typically apply uniform protection, overlooking individual behavioral differences and often degrading data utility. In practice, privacy exposure varies substantially across users and is strongly influenced by behavioral traits, as shown in our previous work [hal-05057826v1]. This motivates the need for adaptive privacy-preserving mechanisms that account for individual exposure levels, enabling tailored protection while preserving the utility of mobility-driven services.
PRISM aims to develop tailored privacy-preserving techniques by leveraging interpretable behavioral exposure modeling. Building on prior work [hal-05288506v1], the project will quantify exposure across multiple dimensions to characterize individual privacy risk. This interpretability will guide the design of adaptive protection mechanisms, both AI-based and non-AI-based, while preserving mobility-driven services and supporting privacy-aware, perceptive Internet edge networks. The datasets to be used in this research proposal will consist of publicly available cellular datasets (e.g., Shenzhen CDRs) and other non-public datasets to which the team has signed NDAs (e.g., Shanghai CDR).
