AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram Model

• URL: http://arxiv.org/abs/2503.21426v1
• Date: Thu, 27 Mar 2025 12:13:28 GMT
• Title: AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram Model
• Authors: Sen Zhang, Qingqing Ye, Haibo Hu, Jianliang Xu,
• Abstract summary: We present AdvSGM, a differentially private skip-gram for graphs via adversarial training.<n>Our core idea is to leverage adversarial training to privatize skip-gram while improving its utility.
• Score: 21.78459506259644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The skip-gram model (SGM), which employs a neural network to generate node vectors, serves as the basis for numerous popular graph embedding techniques. However, since the training datasets contain sensitive linkage information, the parameters of a released SGM may encode private information and pose significant privacy risks. Differential privacy (DP) is a rigorous standard for protecting individual privacy in data analysis. Nevertheless, when applying differential privacy to skip-gram in graphs, it becomes highly challenging due to the complex link relationships, which potentially result in high sensitivity and necessitate substantial noise injection. To tackle this challenge, we present AdvSGM, a differentially private skip-gram for graphs via adversarial training. Our core idea is to leverage adversarial training to privatize skip-gram while improving its utility. Towards this end, we develop a novel adversarial training module by devising two optimizable noise terms that correspond to the parameters of a skip-gram. By fine-tuning the weights between modules within AdvSGM, we can achieve differentially private gradient updates without additional noise injection. Extensive experimental results on six real-world graph datasets show that AdvSGM preserves high data utility across different downstream tasks.

Related papers

• Independent Distribution Regularization for Private Graph Embedding [55.24441467292359]
  Graph embeddings are susceptible to attribute inference attacks, which allow attackers to infer private node attributes from the learned graph embeddings. To address these concerns, privacy-preserving graph embedding methods have emerged. We propose a novel approach called Private Variational Graph AutoEncoders (PVGAE) with the aid of independent distribution penalty as a regularization term.
  arXiv  Detail & Related papers  (2023-08-16T13:32:43Z)
• Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection [38.96828804683783]
  GNNs can inadvertently expose sensitive user information and interactions through their model predictions. Applying standard DP approaches to GNNs directly is not advisable due to two main reasons. We propose a new framework termed Graph Differential Privacy (GDP), specifically tailored to graph learning.
  arXiv  Detail & Related papers  (2023-07-12T19:29:06Z)
• ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees [8.79398901328539]
  Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns. We propose a new differentially private GNN called ProGAP that uses a progressive training scheme to improve such accuracy-privacy trade-offs.
  arXiv  Detail & Related papers  (2023-04-18T12:08:41Z)
• Privacy-Preserved Neural Graph Similarity Learning [99.78599103903777]
  We propose a novel Privacy-Preserving neural Graph Matching network model, named PPGM, for graph similarity learning. To prevent reconstruction attacks, the proposed model does not communicate node-level representations between devices. To alleviate the attacks to graph properties, the obfuscated features that contain information from both vectors are communicated.
  arXiv  Detail & Related papers  (2022-10-21T04:38:25Z)
• 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)
• MentorGNN: Deriving Curriculum for Pre-Training GNNs [61.97574489259085]
  We propose an end-to-end model named MentorGNN that aims to supervise the pre-training process of GNNs across graphs. We shed new light on the problem of domain adaption on relational data (i.e., graphs) by deriving a natural and interpretable upper bound on the generalization error of the pre-trained GNNs.
  arXiv  Detail & Related papers  (2022-08-21T15:12:08Z)
• Differentially Private Graph Classification with GNNs [5.830410490229634]
  Graph Networks (GNNs) have established themselves as the state-of-the-art models for many machine learning applications. We introduce differential privacy for graph-level classification, one of the key applications of machine learning on graphs. We show results on a variety of synthetic and public datasets and evaluate the impact of different GNN architectures.
  arXiv  Detail & Related papers  (2022-02-05T15:16:40Z)
• Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence [73.14373832423156]
  We propose DP-Sinkhorn, a novel optimal transport-based generative method for learning data distributions from private data with differential privacy. Unlike existing approaches for training differentially private generative models, we do not rely on adversarial objectives.
  arXiv  Detail & Related papers  (2021-11-01T18:10:21Z)
• 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)
• Locally Private Graph Neural Networks [12.473486843211573]
  We study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private. We develop a privacy-preserving, architecture-agnostic GNN learning algorithm with formal privacy guarantees. Experiments conducted over real-world datasets demonstrate that our method can maintain a satisfying level of accuracy with low privacy loss.
  arXiv  Detail & Related papers  (2020-06-09T22:36:06Z)

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.