Adam Dziedzic on Private Prompt Learning for Large Language Models

Friday, April 12, 2024 1:00 pm - 2:00 pm EDT (GMT -04:00)

Title: Private Prompt Learning for Large Language Models

Speaker: Adam Dziedzic
Date: Friday, April 12th, 2024
Time: 1:00 PM - 2:00 PM
Location: DC 1302

Zoom Link: Click Here to Join!


Abstract: Large language models (LLMs) are excellent in-context learners. However, the sensitivity of data contained in prompts raises privacy concerns. Our work first shows that these concerns are valid: we instantiate a simple but highly effective membership inference attack against the data used to prompt LLMs. To address this vulnerability, one could forego prompting and resort to fine-tuning LLMs with known algorithms for private gradient descent. However, this comes at the expense of the practicality and efficiency offered by prompting. Therefore, we propose to privately learn to prompt. We first show that soft prompts can be obtained privately through gradient descent on downstream data. However, this is not the case for discrete prompts. Thus, we orchestrate a noisy vote among an ensemble of LLMs presented with different prompts, i.e., a flock of stochastic parrots. The vote privately transfers the flock’s knowledge into a single public prompt. We show that LLMs prompted with our private algorithms closely match the non-private baselines.

Speakers Bio: Adam is a Tenure Track Faculty Member at CISPA Helmholtz Center for Information Security, co-leading the SprintML group. His research is focused on secure and trustworthy Machine Learning as a Service (MLaaS). Adam designs robust and reliable machine learning methods for training and inference of ML models while preserving data privacy and model confidentiality. Adam was a Postdoctoral Fellow at the Vector Institute and the University of Toronto, and a member of the CleverHans Lab, advised by Prof. Nicolas Papernot. He earned his PhD at the University of Chicago, where he was advised by Prof. Sanjay Krishnan and worked on input and model compression for adaptive and robust neural networks. Adamobtained his Bachelor's and Master's degrees from Warsaw University of Technology in Poland. He was also studying at DTU (Technical University of Denmark) and carried out research at EPFL, Switzerland. Adam also worked at CERN (Geneva, Switzerland), Barclays Investment Bank in London (UK), Microsoft Research (Redmond, USA), and Google (Madison, USA).

Franziska is a tenure-track faculty at the CISPA Helmholtz Center for Information Security where she co-leads the SprintML lab. Before, she was a Postdoctoral Fellow at the University of Toronto and Vector Institute advised by Prof. Nicolas Papernot. Her current research centers around private and trustworthy machine learning. Franziska obtained her Ph.D. at the Computer Science Department at Freie University Berlin, where she pioneered the notion of individualized privacy in machine learning. During her Ph.D., Franziska was a research associate at the Fraunhofer Institute for Applied and Integrated Security (AISEC), Germany. She received a Fraunhofer TALENTA grant for outstanding female early career researchers, the German Industrial Research Foundation prize for her research on machine learning privacy, and the Fraunhofer ICT Dissertation award 2023.