Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging open problem. The leading method mainly addresses the local …
Many real-world datasets can be naturally represented as graphs, spanning a wide range of domains. However, the increasing complexity and size of graph datasets present significant …
Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics. The key idea is to recursively propagate and aggregate information along the edges of the given …
Differentially private algorithms allow large-scale data analytics while preserving user privacy. Designing such algorithms for graph data is gaining importance with the growth of …
Differentially private algorithms protect individuals in data analysis scenarios by ensuring that there is only a weak correlation between the existence of the user in the data and the …
Differentially private statistical estimation has seen a flurry of developments over the last several years. Study has been divided into two schools of thought, focusing on empirical …
In this work, we study the applications of differential privacy (DP) in the context of graph- structured data. We discuss the formulations of DP applicable to the publication of graphs …
C Fan, P Li, X Li - Advances in Neural Information …, 2022 - proceedings.neurips.cc
Releasing all pairwise shortest path (APSP) distances between vertices on general graphs under weight Differential Privacy (DP) is known as a challenging task. In previous work, to …
S Zhang, W Ni, N Fu - Computers & Security, 2021 - Elsevier
The goal of privacy-preserving graph publishing is to protect individual privacy in released graph data while preserving data utility. Degree distribution, serving as fundamental …