About Me

Jianqiao Mo 08-09-2020

Email:   jqmo@nyu,edu

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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 accelerators, cryptography, machine learning and security.

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.

Resume, Github, Linkedin, GoogleScholar, ORCID

Reference:

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