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VERSION:2.0
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CALSCALE:GREGORIAN
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UID:c1a54237-e0a4-4a41-b574-2188c731af50@eventxplore
DTSTAMP:20260928T214420Z
DTSTART:20261015T201500Z
DTEND:20261015T211500Z
SUMMARY:ORC Seminar: OR with a White Hat: Evidencing Privacy Vulnerabilities in ML Models
DESCRIPTION: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
LOCATION:Building E51
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