Fall 2023 CYSE and C4I Joint Distinguished Seminar Series
Designing Neural Networks for Efficient Encrypted Inference
Dr. Chinmay Hegde
Associate Professor, Joint appointment with the Department of Electrical and Computer Engineering and Computer Science and Engineering,
New York University
Founder, DICE (Data, Intelligence, and Computation in Engineering) Lab
December 7, 2023, 10:00 am – 11.00 am
C4I Center, Room # 4705, Nguyen Engineering Building
Zoom: Link
Abstract: As modern AI models become ever more pervasive in society, so too are concerns surrounding users’ data privacy. Curiously, standard cryptographic encryption approaches for guaranteeing data privacy do not interact well with traditional neural network models. In this talk, I will (a) outline why standard networks are not encryption-efficient, (b) suggest three new approaches for designing deep networks that do support efficient and secure inference, (c) show results instantiating these approaches on real-world use cases, and (d) discuss theoretical approaches for understanding the limits of private inference.
Bio: Chinmay Hegde is an Associate Professor at NYU, jointly appointed with the CSE and ECE Departments. His research focuses on foundational aspects of machine learning (such as reliability, robustness, efficiency, and privacy). He also works on applications ranging from computational imaging, materials design, plant science, and cybersecurity. He is a recipient of the NSF CAREER and CRII awards, multiple teaching awards, and best paper awards at ICML, SPARS, and MMLS.
Date/Time
12/07/2023
10:00 am - 11:00 am
Location
Engineering Building Room 4705
