Privacy-Preserved Learning on Graph-Structured Data

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Privacy-Preserved Learning on Graph-Structured Data"

By

Mr. Qi HU


Abstract:

Graph-structured data and graph learning techniques, particularly graph 
neural networks (GNN), have revolutionized numerous applications in recent 
years. These advancements enable sophisticated knowledge extraction from 
complex graph data, unlocking unprecedented opportunities for discovery and 
decision-making. However, the growing accessibility of graph data and the 
increasing complexity of graph learning models have raised significant 
privacy concerns that remain inadequately addressed despite ongoing research 
efforts.

This thesis investigates privacy preservation in graph learning models, 
focusing on developing effective and robust approaches to mitigate these 
concerns. In the thesis, we examine privacy preservation in traditional 
graph learning models, address privacy challenges in neural graph databases 
(NGDBs), and extend privacy preservation to distributed scenarios.


Date:                   Thursday, 3 April 2025

Time:                   10:00am - 12:00noon

Venue:                  Room 3494
                        Lifts 25/26

Chairman:               Prof. Ling SHI (ECE)

Committee Members:      Dr. Yangqiu SONG (Supervisor)
                        Prof. Raymond WONG
                        Dr. Dongdong SHE
                        Dr. Dong XIA (MATH)
                        Prof. Sinno Jialin PAN (CUHK)