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Plog: An Efficient and Privacy-Preserving Collaborative Learning Framework on Vertically Partitioned Graph Data
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DOI：10.1109/TKDE.2026.3676441.png)
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Abstract
En 中文
With the rapid advancement and widespread application of the graph neural network (GNN), the collaborative graph learning, in which multiple parties collaboratively construct a GNN model using their respective graph data, has attracted increasing attention. However, this paradigm also raises significant privacy concerns, as both nodes and edges may contain sensitive personal information, while existing privacy-preserving schemes often come at the cost of degraded model performance or substantial system overhead. Therefore, this paper proposes an efficient and privacy-preserving collaborative learning framework on vertically partitioned graph data, dubbed Plog. Specifically, we first design a decomposition algorithm to split the sparse adjacency matrix into the summation of multiple independent permutations, which are lightweight, parallelizable, and well-suited for secure multi-party computation. Building on this, a weighted oblivious batch permutation protocol is carefully customized based on correlated randomness to securely and efficiently compute adjacency matrix multiplications, addressing the core efficiency bottleneck in GNN inference and training. The selective security of Plog is formally verified under the ideal-real paradigm. Extensive experimental results on three real-world datasets demonstrate that compared to the state-of-the-art scheme, Plog can reduce online communication rounds by $\bm {46\%}$ and achieve a $\bm {1.73 \times }$ speedup in the overall inference and training time.
Keywords:
Graph neural network
secure multi-party computation
collaborative learning
Journal
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10.4
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