ORC Seminar: OR with a White Hat: Evidencing Privacy Vulnerabilities in ML Models

Thibaut Vidal Professor École Polytechnique de Montréal Abstract: The deployment of machine learning models in high-risk domains (e.g., finance, medicine) raises questions about the privacy of the data used to train them. In this talk, I will show that operations research methods can provide a rigorous methodological backbone for auditing and mitigating certain privacy risks in ML models. I will first discuss a white-box reconstruction attack that formulates the recovery of a random forest’s training data as a combinatorial problem solved with constraint programming. This approach reconstructs