Gautam Kamath and colleagues receive runner-up 2026 Caspar Bowden PET Award
Professor Gautam Kamath and his international collaborators from industry and academia have received the runner-up Caspar Bowden Award at PETS 2026, the 26th Privacy Enhancing Technologies Symposium, held this year in Calgary from July 20 to 25.
Their paper, Differentially Private Fine-tuning of Language Models, demonstrates that advances in natural language processing, parameter efficiency, privacy accounting, and large language models can be combined to enable private fine-tuning of models with performance that approaches that of non-private models.