Privacy-preserving machine learning based on homomorphic encryption

PhD Qualifying Examination


Title: "Privacy-preserving machine learning based on homomorphic encryption"

by

Mr. Yiteng PENG


Abstract:

The rapid deployment of machine learning (ML) systems in real-world 
scenarios, particularly in sensitive domains such as healthcare and finance, 
has raised significant privacy concerns regarding data exposure and privacy 
leakage during both model training and inference processes. Homomorphic 
encryption (HE), a kind of cryptographic scheme that allows computations to 
be performed directly on encrypted data, is considered as a promising 
solution to address these privacy challenges. However, the practical 
adoption of HE in ML faces significant obstacles, due to the limited range 
of supported operations in current HE schemes and the substantial 
computational overhead in encrypted computations. To overcome these hurdles, 
recent research focuses on developing efficient adaptations and 
optimizations techniques. For further facilitating HE integration into ML 
workflows with these innovative techniques, several HE-ML frameworks have 
also been developed to enable data scientists to leverage HE's benefits 
without extensive cryptographic expertise.

In this survey, we present a comprehensive review of privacy-preserving ML 
based on HE. We begin with the fundamental concepts of HE and its 
applications across diverse ML models. We then examine current adaptation 
and optimization techniques that enhance the compatibility and efficiency of 
HE within ML contexts. Finally, we discuss emerging frameworks that abstract 
the complexities of HE-ML. By synthesizing current knowledge, this survey 
aims to provide valuable insights for applying HE in ML systems and to 
stimulate future research in this field.


Date:                   Friday, 25 July 2025

Time:                   4:00pm - 6:00pm

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Dr. Shuai Wang (Supervisor)
                        Dr. Xiaomin Ouyang (Chairperson)
                        Dr. Binhang Yuan