The defense will be held in the Amphitheater, Chappe Library UCBL, and will be streamed live here.
Privacy in learning systems for healthcare
With the development of the Internet of Things (IoT), smartphones and sensors are now able to provide information about the user’s activity and even their physiology. This has led to a growing interest from the scientific community, particularly in the field of e-health, with applications in the monitoring of patients undergoing rehabilitation in order to offer more personalized follow-up. However, in addition to guiding the rehabilitation process, the generation and transmission of IoT data is also vulnerable to privacy breaches. Indeed, the complex processing chain of the IoT application in healthcare multiplies the risk of privacy threats throughout the life cycle of IoT data, including collection, transmission and storage, by an adversary who can retrieve the data and re-identify or reveal sensitive patient information. This thesis focuses on the following questions: Is the data collected sufficiently protected so that no one can misuse it to re-identify the owner or infer sensitive information? Is the protected data still accurate enough for healthcare applications such as rehabilitation? Achieving balance between data utility and privacy protection is an important challenge that we explore in this thesis from different angles. More specifically, the first part focuses on the problem of data anonymization through minimization, while the second part focuses on preventing the inference of sensitive attributes through a Generative Adversarial Networks (GAN) to sanitize sensor data and an approach exploiting private layers in Federated Learning (FL).
- Fossati, Caroline Professeure des Universités, Institut Fresnel Rapporteure
- Vincent, Emmanuel Directeur de Recherche, INRIA Nancy Rapporteur
- Bellet, Aurélien Chargé de Recherche, INRIA Lille Examinateur
- Ben Mokhtar, Sonia Directrice de Recherche, LIRIS Examinatrice
- Dieterlen, Alain Professeur des Universités, IRIMAS Examinateur
- Frindel, Carole Maître de Conférences, INSA Lyon Co-directrice de thèse
- Boutet, Antoine Maître de Conférences, INSA Lyon Co-directeur de thèse