Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphs
This addresses privacy concerns in distributed graph learning for data mining and machine learning applications, but it is incremental as it builds on existing methods like GW distance and LDP.
The paper tackles the problem of learning similarity between distributed structural graphs while preserving privacy, by proposing a framework that combines Gromov-Wasserstein discrepancy with local differential privacy, achieving comparable or better performance in classification and clustering tasks under strong privacy guarantees.
Learning the similarity between structured data, especially the graphs, is one of the essential problems. Besides the approach like graph kernels, Gromov-Wasserstein (GW) distance recently draws big attention due to its flexibility to capture both topological and feature characteristics, as well as handling the permutation invariance. However, structured data are widely distributed for different data mining and machine learning applications. With privacy concerns, accessing the decentralized data is limited to either individual clients or different silos. To tackle these issues, we propose a privacy-preserving framework to analyze the GW discrepancy of node embedding learned locally from graph neural networks in a federated flavor, and then explicitly place local differential privacy (LDP) based on Multi-bit Encoder to protect sensitive information. Our experiments show that, with strong privacy protections guaranteed by the $\varepsilon$-LDP algorithm, the proposed framework not only preserves privacy in graph learning but also presents a noised structural metric under GW distance, resulting in comparable and even better performance in classification and clustering tasks. Moreover, we reason the rationale behind the LDP-based GW distance analytically and empirically.