When Differential Privacy Meets Graph Neural Networks
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by
Sina Sajadmanesh, Daniel Gatica-Perez
2020
Abstract
Graph Neural Networks have demonstrated superior performance in learning
graph representations for several subsequent downstream inference tasks.
However, learning over graph data types can raise privacy concerns when nodes
represent people or human-related variables that involve personal information
about individuals. Previous works have presented various techniques for
privacy-preserving deep learning over non-relational data, such as image,
audio, video, and text, but there is less work addressing the privacy issues
involved in applying deep learning algorithms on graphs. As a result and for
the first time, in this paper, we develop a privacy-preserving learning
algorithm with formal privacy guarantees for Graph Convolutional Networks
(GCNs) based on Local Differential Privacy (LDP) to tackle the problem of
node-level privacy, where graph nodes have potentially sensitive features that
need to be kept private, but they could be beneficial for learning rich node
representations in a centralized learning setting. Specifically, we propose an
LDP algorithm in which a central server can communicate with graph nodes to
privately collect their data and estimate the graph convolution layer of a GCN.
We then analyze the theoretical characteristics of the method and compare it
with state-of-the-art mechanisms. Experimental results over real-world graph
datasets demonstrate the effectiveness of the proposed method for both
privacy-preserving node classification and link prediction tasks and verify our
theoretical findings.
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