Privacy-Preserving Graph Machine Learning from Data to Computation: A
  Survey
Dongqi Fu Wenxuan Bao Ross Maciejewski Hanghang Tong Jingrui He
In graph machine learning, data collection, sharing, and analysis often
involve multiple parties, each of which may require varying levels of data
security and privacy. To this end, preserving privacy is of great importance in
protecting sensitive information. In the era of big data, the relationships
among data entities have become unprecedentedly complex, and more applications
utilize advanced data structures (i.e., graphs) that can support network
structures and relevant attribute information. To date, many graph-based AI
models have been proposed (e.g., graph neural networks) for various domain
tasks, like computer vision and natural language processing. In this paper, we
focus on reviewing privacy-preserving techniques of graph machine learning. We
systematically review related works from the data to the computational aspects.
We first review methods for generating privacy-preserving graph data. Then we
describe methods for transmitting privacy-preserved information (e.g., graph
model parameters) to realize the optimization-based computation when data
sharing among multiple parties is risky or impossible. In addition to
discussing relevant theoretical methodology and software tools, we also discuss
current challenges and highlight several possible future research opportunities
for privacy-preserving graph machine learning. Finally, we envision a unified
and comprehensive secure graph machine learning system.