GRAIN: Exact Graph Reconstruction from Gradients

• URL: http://arxiv.org/abs/2503.01838v1
• Date: Mon, 03 Mar 2025 18:58:12 GMT
• Title: GRAIN: Exact Graph Reconstruction from Gradients
• Authors: Maria Drencheva, Ivo Petrov, Maximilian Baader, Dimitar I. Dimitrov, Martin Vechev,
• Abstract summary: Federated learning claims to enable collaborative model training among multiple clients with data privacy.<n>Recent studies have shown the client privacy is still at risk due to the, so called, gradient inversion attacks.<n>We present GRAIN, the first exact gradient inversion attack on graph data in the honest-but-curious setting.
• Score: 5.697251900862886
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Federated learning claims to enable collaborative model training among multiple clients with data privacy by transmitting gradient updates instead of the actual client data. However, recent studies have shown the client privacy is still at risk due to the, so called, gradient inversion attacks which can precisely reconstruct clients' text and image data from the shared gradient updates. While these attacks demonstrate severe privacy risks for certain domains and architectures, the vulnerability of other commonly-used data types, such as graph-structured data, remain under-explored. To bridge this gap, we present GRAIN, the first exact gradient inversion attack on graph data in the honest-but-curious setting that recovers both the structure of the graph and the associated node features. Concretely, we focus on Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) -- two of the most widely used frameworks for learning on graphs. Our method first utilizes the low-rank structure of GNN gradients to efficiently reconstruct and filter the client subgraphs which are then joined to complete the input graph. We evaluate our approach on molecular, citation, and social network datasets using our novel metric. We show that GRAIN reconstructs up to 80% of all graphs exactly, significantly outperforming the baseline, which achieves up to 20% correctly positioned nodes.

Related papers

• Gradient Inversion Attack on Graph Neural Networks [11.735290341808064]
  Malicious attacker can steal private image data from exchanging gradient of neural networks during federated learning.<n>Two widely-used GNN frameworks are analyzed, namely GCN and GraphSAGE.<n>It is shown that parts of the graph data can be leaked from the gradients.
  arXiv  Detail & Related papers  (2024-11-29T02:42:17Z)
• Federated Graph Learning with Graphless Clients [52.5629887481768]
  Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs) We propose a novel framework FedGLS to tackle the problem in FGL with graphless clients.
  arXiv  Detail & Related papers  (2024-11-13T06:54:05Z)
• OpenGraph: Towards Open Graph Foundation Models [20.401374302429627]
  Graph Neural Networks (GNNs) have emerged as promising techniques for encoding structural information. Key challenge remains: the difficulty of generalizing to unseen graph data with different properties. We propose a novel graph foundation model, called OpenGraph, to address this challenge.
  arXiv  Detail & Related papers  (2024-03-02T08:05:03Z)
• Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation [153.92387500677023]
  We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations. The proposed graph Transformer encoder combines graph convolutions and self-attentions in a Transformer to model both local and global interactions. We also propose a novel self-guided pre-training method for graph representation learning.
  arXiv  Detail & Related papers  (2024-01-15T14:36:38Z)
• EGRC-Net: Embedding-induced Graph Refinement Clustering Network [66.44293190793294]
  We propose a novel graph clustering network called Embedding-Induced Graph Refinement Clustering Network (EGRC-Net) EGRC-Net effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance. Our proposed methods consistently outperform several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-19T09:08:43Z)
• Model Inversion Attacks against Graph Neural Networks [65.35955643325038]
  We study model inversion attacks against Graph Neural Networks (GNNs) In this paper, we present GraphMI to infer the private training graph data. Our experimental results show that such defenses are not sufficiently effective and call for more advanced defenses against privacy attacks.
  arXiv  Detail & Related papers  (2022-09-16T09:13:43Z)
• Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping [116.26579152942162]
  Graph neural networks (GNNs) are processing architectures that exploit graph structural information to model representations from network data. Despite their success, GNNs suffer from sub-optimal generalization performance given limited training data. This paper proposes Topology Adaptive Edge Dropping to improve generalization performance and learn robust GNN models.
  arXiv  Detail & Related papers  (2021-06-05T13:20:36Z)
• GraphMI: Extracting Private Graph Data from Graph Neural Networks [59.05178231559796]
  We present textbfGraph textbfModel textbfInversion attack (GraphMI), which aims to extract private graph data of the training graph by inverting GNN. Specifically, we propose a projected gradient module to tackle the discreteness of graph edges while preserving the sparsity and smoothness of graph features. We design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
  arXiv  Detail & Related papers  (2021-06-05T07:07:52Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• Co-embedding of Nodes and Edges with Graph Neural Networks [13.020745622327894]
  Graph embedding is a way to transform and encode the data structure in high dimensional and non-Euclidean feature space. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. Our approach achieves or matches the state-of-the-art performance in four graph learning tasks.
  arXiv  Detail & Related papers  (2020-10-25T22:39:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.