Building Secure Collaborative Systems via Systems and Cryptography Co-Design
Speaker
Wenting Zheng
Carnegie Mellon University (CMU)
Host
Ryan Lehmkuhl
Abstract
The recent revolution in advanced data analytics and machine learning have made it possible to extract unprecedented value from user data. However, this comes at the cost of user privacy in many application workflows. In this talk, I will discuss some ideas around building systems that enable privacy-preserving computation via a co-design of systems and cryptography. In the first part of the talk, I will present Bolt (IEEE S&P 2024), a new system for privacy-preserving two-party inference for a large language model like BERT using secure multiparty computation (MPC). With our system, a user can safely outsource prediction to a third party without revealing their sensitive data and or learning about the third party’s proprietary model parameters. In the second part, I will talk about building systems for democratizing cryptography. In Silph (IEEE S&P 2023), we develop a framework that can automatically compile a program written in a high-level language to an optimized, hybrid MPC protocol that mixes multiple MPC primitives securely and efficiently. This makes it possible for any programmer with no expertise in cryptography to create efficient MPC protocols from scratch.
Bio
Wenting Zheng is an assistant professor in the Computer Science Department at CMU. Her research interests are in computer systems, security, and applied cryptography. She aims to bridge the gap between theory and practice through a co-design of cryptography and systems. She does so by building practical cryptosystems with provable security guarantees, designing novel cryptographic primitives and protocols, and building systems for democratizing and accelerating cryptography. She is a recipient of NSF CAREER Award, Google Research Scholar Award, Distinguished Paper Award at IEEE Euro S&P, IBM PhD Fellowship, and Berkeley Fellowship. She obtained her Ph.D. in EECS from UC Berkeley.
The recent revolution in advanced data analytics and machine learning have made it possible to extract unprecedented value from user data. However, this comes at the cost of user privacy in many application workflows. In this talk, I will discuss some ideas around building systems that enable privacy-preserving computation via a co-design of systems and cryptography. In the first part of the talk, I will present Bolt (IEEE S&P 2024), a new system for privacy-preserving two-party inference for a large language model like BERT using secure multiparty computation (MPC). With our system, a user can safely outsource prediction to a third party without revealing their sensitive data and or learning about the third party’s proprietary model parameters. In the second part, I will talk about building systems for democratizing cryptography. In Silph (IEEE S&P 2023), we develop a framework that can automatically compile a program written in a high-level language to an optimized, hybrid MPC protocol that mixes multiple MPC primitives securely and efficiently. This makes it possible for any programmer with no expertise in cryptography to create efficient MPC protocols from scratch.
Bio
Wenting Zheng is an assistant professor in the Computer Science Department at CMU. Her research interests are in computer systems, security, and applied cryptography. She aims to bridge the gap between theory and practice through a co-design of cryptography and systems. She does so by building practical cryptosystems with provable security guarantees, designing novel cryptographic primitives and protocols, and building systems for democratizing and accelerating cryptography. She is a recipient of NSF CAREER Award, Google Research Scholar Award, Distinguished Paper Award at IEEE Euro S&P, IBM PhD Fellowship, and Berkeley Fellowship. She obtained her Ph.D. in EECS from UC Berkeley.