Multi-View Graph Feature Propagation for Privacy Preservation and Feature Sparsity

• URL: http://arxiv.org/abs/2510.11347v1
• Date: Mon, 13 Oct 2025 12:42:00 GMT
• Title: Multi-View Graph Feature Propagation for Privacy Preservation and Feature Sparsity
• Authors: Etzion Harari, Moshe Unger,
• Abstract summary: Graph Neural Networks (GNNs) have demonstrated remarkable success in node classification tasks over relational data.<n>Feature matrices are highly sparse or contain sensitive information, leading to degraded performance and increased privacy risks.<n>We propose a novel Multi-view Feature Propagation framework that enhances node classification under feature sparsity while promoting privacy preservation.
• Score: 0.45835414225547183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable success in node classification tasks over relational data, yet their effectiveness often depends on the availability of complete node features. In many real-world scenarios, however, feature matrices are highly sparse or contain sensitive information, leading to degraded performance and increased privacy risks. Furthermore, direct exposure of information can result in unintended data leakage, enabling adversaries to infer sensitive information. To address these challenges, we propose a novel Multi-view Feature Propagation (MFP) framework that enhances node classification under feature sparsity while promoting privacy preservation. MFP extends traditional Feature Propagation (FP) by dividing the available features into multiple Gaussian-noised views, each propagating information independently through the graph topology. The aggregated representations yield expressive and robust node embeddings. This framework is novel in two respects: it introduces a mechanism that improves robustness under extreme sparsity, and it provides a principled way to balance utility with privacy. Extensive experiments conducted on graph datasets demonstrate that MFP outperforms state-of-the-art baselines in node classification while substantially reducing privacy leakage. Moreover, our analysis demonstrates that propagated outputs serve as alternative imputations rather than reconstructions of the original features, preserving utility without compromising privacy. A comprehensive sensitivity analysis further confirms the stability and practical applicability of MFP across diverse scenarios. Overall, MFP provides an effective and privacy-aware framework for graph learning in domains characterized by missing or sensitive features.

Related papers

• Taipan: A Query-free Transfer-based Multiple Sensitive Attribute Inference Attack Solely from Publicly Released Graphs [4.838500914184325]
  We introduce textbfTaipan, the first query-free transfer-based attack framework for multiple sensitive attribute inference attacks on graphs.<n>Experiments on diverse real-world graph datasets demonstrate that Taipan consistently achieves strong attack performance across same-distribution settings.<n>Our findings underscore the urgent need for more robust multi-attribute privacy-preserving graph publishing methods and data-sharing practices.
  arXiv  Detail & Related papers  (2026-02-06T13:37:24Z)
• Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework [57.04850867402913]
  Federated clustering addresses the challenge of extracting patterns from decentralized, unlabeled data.<n>We propose Structural Privacy-Preserving Federated Graph Clustering (SPP-FGC), a novel algorithm that innovatively leverages local structural graphs as the primary medium for privacy-preserving knowledge sharing.<n>Our framework achieves state-of-the-art performance, improving clustering accuracy by up to 10% (NMI) over federated baselines while maintaining provable privacy guarantees.
  arXiv  Detail & Related papers  (2025-11-14T03:05:22Z)
• Subgraph Federated Learning via Spectral Methods [52.40322201034717]
  FedLap is a novel framework that captures inter-node dependencies while ensuring privacy and scalability.<n>We provide a formal analysis of the privacy of FedLap, demonstrating that it preserves privacy.
  arXiv  Detail & Related papers  (2025-10-29T16:22:32Z)
• MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning [1.8124328823188354]
  This paper introduces a framework for multi-scale graph network embedding based on spectral graph wavelets.<n>A key advantage of the proposed embedding is its ability to establish a correspondence between the embedding and input feature spaces.<n>We validate the effectiveness of our graph embedding framework on multiple public datasets across various downstream tasks.
  arXiv  Detail & Related papers  (2024-06-04T20:48:33Z)
• Deep Common Feature Mining for Efficient Video Semantic Segmentation [25.851900402539467]
  We present Deep Common Feature Mining (DCFM) for video semantic segmentation.<n>DCFM explicitly decomposes features into two complementary components.<n>We incorporate a self-supervised loss function to reinforce intra-class feature similarity and enhance temporal consistency.
  arXiv  Detail & Related papers  (2024-03-05T06:17:59Z)
• Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification [51.04894019092156]
  Federated learning (FL) has been recognized as a rapidly growing area, where the model is trained over clients under the FL orchestration (PS) In this paper, we propose a novel primal sparification algorithm for and guarantee non-smooth FL problems. Its unique insightful properties and its analyses are also presented.
  arXiv  Detail & Related papers  (2023-10-30T14:15:47Z)
• Privacy-Preserving Graph Embedding based on Local Differential Privacy [26.164722283887333]
  We introduce a novel privacy-preserving graph embedding framework, named PrivGE, to protect node data privacy. Specifically, we propose an LDP mechanism to obfuscate node data and utilize personalized PageRank as the proximity measure to learn node representations. Experiments on several real-world graph datasets demonstrate that PrivGE achieves an optimal balance between privacy and utility.
  arXiv  Detail & Related papers  (2023-10-17T08:06:08Z)
• 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)
• RARE: Robust Masked Graph Autoencoder [45.485891794905946]
  Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-training (SGP) paradigm. We propose a novel SGP method termed Robust mAsked gRaph autoEncoder (RARE) to improve the certainty in inferring masked data.
  arXiv  Detail & Related papers  (2023-04-04T03:35:29Z)
• FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations [53.268801169075836]
  We propose FedLAP-DP, a novel privacy-preserving approach for federated learning. A formal privacy analysis demonstrates that FedLAP-DP incurs the same privacy costs as typical gradient-sharing schemes. Our approach presents a faster convergence speed compared to typical gradient-sharing methods.
  arXiv  Detail & Related papers  (2023-02-02T12:56:46Z)
• Handling Distribution Shifts on Graphs: An Invariance Perspective [78.31180235269035]
  We formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM) EERM resorts to multiple context explorers that are adversarially trained to maximize the variance of risks from multiple virtual environments. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution.
  arXiv  Detail & Related papers  (2022-02-05T02:31:01Z)
• Information Obfuscation of Graph Neural Networks [96.8421624921384]
  We study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data. We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance.
  arXiv  Detail & Related papers  (2020-09-28T17:55:04Z)
• Secure Deep Graph Generation with Link Differential Privacy [32.671503863933616]
  We leverage the differential privacy (DP) framework to formulate and enforce rigorous privacy constraints on deep graph generation models. In particular, we enforce edge-DP by injecting proper noise to the gradients of a link reconstruction-based graph generation model. Our proposed DPGGAN model is able to generate graphs with effectively preserved global structure and rigorously protected individual link privacy.
  arXiv  Detail & Related papers  (2020-05-01T15:49:17Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

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.