About Me

Jianqiao Mo 08-09-2020

Email:   jqmo@nyu,edu

Welcome to Jianqiao Mo’s page! I’m an ECE PhD student at New York University (BAAHL), advised by Brandon Reagen and also collaborating with Siddharth Garg.

Keywords: computer architecture, hardware-software co-design, privacy-preserving computation, zero knowledge proofs, machine learning.

Resume, Github, Linkedin, GoogleScholar, ORCID

Research: My research topics / projects.

Recent Activities: My recent research activities and latest updates.

My research focuses on hardware acceleration for cryptography, with the goal of advancing end-to-end data privacy and integrity.

Privacy-Preserving Computation for End-To-End Data Privacy

I am developing specialized hardware systems to accelerate privacy-preserving computation (secure multi-party computation).

My work includes accelerators for cryptographic protocols (particularly Garbled Circuits (GC) [1,2]).

It is useful for enabling “private inference” in machine learning.

PPC system
       Figure: Applying privacy-preserving computation to private inference.

Zero-Knowledge Proofs for Data Integrity

I am also working on hardware accelerators for zero-knowledge proofs (zkp), which allow the verification of data integrity by proving the correctness of computations without revealing the underlying data.

ZKP system
       Figure: An example of zero-knowledge proof.

Before NYU, I earned my Bachelor’s degree from Nanjing University where I worked as a research intern advised by Prof. Zhongfeng Wang. My research there focused on reducing the computational cost of deep neural networks by optimizing dynamic exit branches.

Reference:

[1] Yakoubov, Sophia. “A gentle introduction to yao’s garbled circuits.” (2017).
[2] Navarro, Ignacio. “On Garbled Circuits.” (2018).