International Association
for Cryptologic Research

Confidential Inference and Explainability: Toward Self-Diagnosis via Imaging

This PhD topic aims to jointly address privacy and explainability of decisions obtained through image analysis using a neural network. In the context of a classification task performed on a remote server, the goal is to develop approaches that ensure the confidentiality of the explanation as well as that of the input (and output) data. Preserving the privacy of data while ensuring the transparency of the model is a crucial challenge, particularly in domains such as healthcare. The objective aligns with the emerging regulatory framework on AI at the European level (AI Act). While these issues are the subject of significant research individually - whether in applied cryptography or machine learning - the combination of explainability under privacy constraints represents a new research problem. The project will seek to identify local explainability methods based on visual information or concepts that can be adapted to a privacy-preserving mode. Confidentiality may be approached through secure multi-party computation and/or homomorphic encryption. Thanks to a collaboration with the Saint-Etienne University Hospital (France), it will be possible to fine-tune the secure AI system and conduct supervised experiments on health data, aimed at enabling self-diagnosis. The experimentation may also extend to ethical and legal dimensions, through a partnership with the University of Ottawa.

PhD Location: Laboratoire Hubert Curien (LabHC), Université Jean Monnet, Saint-Etienne, France (regular meetings at the CITI Laboratory, INSA Lyon, Villeurbanne, France).

Starting date: 01/10/2025.

Expected profile: Candidates holding a degree from an engineering school or a Master 2 from a university in applied mathematics or computer science, with training in cryptography and machine learning, and proficiency in a programming language and one or more reference development libraries in one of these fields.

Send your CV, cover letter and master transcripts and give contact details of referees by 25/05/2025.