Data Science Seminar

Designing Neural Networks for Efficient Encrypted Inference

Chinmay Hedge

Designing Neural Networks for Efficient Encrypted Inference

When	Friday, January 21, 2022, 3:00 PM – 4:00 PM (MT)

Abstract

As deep neural networks become ever more pervasive, 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 two new approaches for designing deep networks that do support efficient and secure inference, and (c) show results instantiating these approaches on real-world use cases.

Speaker

Chinmay Hedge

NYU

Chinmay is a faculty member in the CSE and ECE Departments at the NYU Tandon School of Engineering. His research focuses on developing principled, fast, and robust algorithms for diverse problems in machine learning, with applications to imaging and computer vision, materials design, and transportation. Prior to NYU, he was an assistant professor in the Electrical and Computer Engineering Department at Iowa State University, and before that, a post-doctoral associate in the Theory of Computation (TOC) group at MIT, working with Piotr Indyk. He received his Ph.D. at Rice University under the supervision of Rich Baraniuk.

Tags: deep learning privacy & security

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