Privacy-preserving design of graph neural networks with applications to
  vertical federated learning
Ruofan Wu Mingyang Zhang Lingjuan Lyu Xiaolong Xu Xiuquan Hao Xinyi Fu Tengfei Liu Tianyi Zhang Weiqiang Wang
The paradigm of vertical federated learning (VFL), where institutions
collaboratively train machine learning models via combining each other's local
feature or label information, has achieved great success in applications to
financial risk management (FRM). The surging developments of graph
representation learning (GRL) have opened up new opportunities for FRM
applications under FL via efficiently utilizing the graph-structured data
generated from underlying transaction networks. Meanwhile, transaction
information is often considered highly sensitive. To prevent data leakage
during training, it is critical to develop FL protocols with formal privacy
guarantees. In this paper, we present an end-to-end GRL framework in the VFL
setting called VESPER, which is built upon a general privatization scheme
termed perturbed message passing (PMP) that allows the privatization of many
popular graph neural architectures.Based on PMP, we discuss the strengths and
weaknesses of specific design choices of concrete graph neural architectures
and provide solutions and improvements for both dense and sparse graphs.
Extensive empirical evaluations over both public datasets and an industry
dataset demonstrate that VESPER is capable of training high-performance GNN
models over both sparse and dense graphs under reasonable privacy budgets.