GraphPub: Generation of Differential Privacy Graph with High Availability
CoRR(2024)
摘要
In recent years, with the rapid development of graph neural networks (GNN),
more and more graph datasets have been published for GNN tasks. However, when
an upstream data owner publishes graph data, there are often many privacy
concerns, because many real-world graph data contain sensitive information like
person's friend list. Differential privacy (DP) is a common method to protect
privacy, but due to the complex topological structure of graph data, applying
DP on graphs often affects the message passing and aggregation of GNN models,
leading to a decrease in model accuracy. In this paper, we propose a novel
graph edge protection framework, graph publisher (GraphPub), which can protect
graph topology while ensuring that the availability of data is basically
unchanged. Through reverse learning and the encoder-decoder mechanism, we
search for some false edges that do not have a large negative impact on the
aggregation of node features, and use them to replace some real edges. The
modified graph will be published, which is difficult to distinguish between
real and false data. Sufficient experiments prove that our framework achieves
model accuracy close to the original graph with an extremely low privacy
budget.
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