インサイト - Machine Learning - # Privacy-Preserving Graph Neural Networks

Edge Private Graph Neural Networks with Singular Value Perturbation: Privacy-Preserving GNN Training Algorithm Eclipse

Q: How does Eclipse compare to other state-of-the-art baselines in terms of privacy protection and model utility

Eclipse outperforms other state-of-the-art baselines in terms of both privacy protection and model utility. In terms of privacy protection, Eclipse provides strong edge-level differential privacy guarantees by perturbing the singular values of the adjacency matrix. This approach ensures that sensitive edge information is protected while still allowing for effective training of graph neural networks (GNNs). Additionally, Eclipse offers better resilience against common edge attacks such as LPA and LINKTELLER compared to DPGCN and LPGNet. When it comes to model utility, Eclipse maintains good performance while providing robust privacy protection. It achieves a significantly better privacy-utility tradeoff compared to existing methods, showing gains in model utility by up to 46% under strong privacy constraints (𝜖 < 4). Even under extreme privacy constraints (𝜖 < 1), Eclipse still maintains higher model utility compared to training with node features only (MLP). Overall, Eclipse strikes a balance between protecting private edges and maintaining high model accuracy.

Q: What are the potential implications of using low-rank decomposition for privacy preservation in GNNs

The use of low-rank decomposition for privacy preservation in GNNs has several potential implications: Reduced Dimensionality: Low-rank decomposition reduces the dimensionality of the graph data by focusing on the most important components captured by the principal bases and singular values. This reduction can lead to more efficient computation and storage requirements. Privacy Enhancement: By leveraging low-rank structures observed in real-world graphs, low-rank decomposition helps protect sensitive edge information while preserving essential topological features necessary for GNN training. Improved Privacy-Utility Tradeoff: The compact representation obtained through low-rank decomposition allows for stronger differential privacy guarantees with minimal impact on model performance. This leads to a better balance between protecting user data and achieving accurate predictions. Resilience Against Attacks: Low-rank decomposition can enhance resilience against adversarial attacks targeting graph structure reconstruction or inference tasks due to reduced exposure of detailed connectivity information. Overall, incorporating low-rank decomposition into GNN training methodologies like Eclipse can offer enhanced privacy protection without compromising model effectiveness.

Q: How can the concept of differential privacy be extended beyond edge-level protection in graph neural networks

Differential Privacy (DP) can be extended beyond edge-level protection in graph neural networks by considering additional levels of granularity within the data: Node-Level DP: Extending DP protections at the node level involves ensuring that individual nodes' attributes are also safeguarded during training and inference processes within GNNs. Graph-Level DP: Protecting entire subgraphs or communities within a larger network could involve applying DP mechanisms at a higher level than just individual edges or nodes. Feature-Level DP: Ensuring that specific features used as input variables in GNN models are also subject to differential privacy measures can further enhance overall data protection. By expanding differential privacy considerations beyond just edges in graph neural networks, comprehensive safeguards can be implemented across various dimensions of graph data analysis tasks for improved security and confidentiality measures.

核心概念

Eclipse introduces a privacy-preserving GNN training algorithm that perturbs singular values, achieving strong privacy protection on edges while maintaining model utility.

要約

Edge Private Graph Neural Networks with Singular Value Perturbation focuses on the development of Eclipse, a novel privacy-preserving GNN training algorithm. Eclipse leverages low-rank decomposition and perturbs singular values to protect sensitive edge information in graph structures. By preserving primary graph topology and reducing residual edges, Eclipse achieves a better privacy-utility tradeoff compared to existing methods. Experimental results demonstrate significant gains in model utility and resilience against common edge attacks.

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統計

Under strong privacy constraints (𝜖 < 4), Eclipse shows up to 46% higher model utility.
Eclipse lowers the attack AUC by up to 5% compared to other baselines under LPA attacks.

引用

"Eclipse maintains good model utility while providing strong privacy protection on edges."
"Eclipse achieves significantly better privacy-utility tradeoff compared to existing methods."

抽出されたキーインサイト

Edge Private Graph Neural Networks with Singular Value Perturbation

by Tingting Tan... 場所 arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10995.pdf

Edge Private Graph Neural Networks with Singular Value Perturbation

深掘り質問

How does Eclipse compare to other state-of-the-art baselines in terms of privacy protection and model utility

Eclipse outperforms other state-of-the-art baselines in terms of both privacy protection and model utility. In terms of privacy protection, Eclipse provides strong edge-level differential privacy guarantees by perturbing the singular values of the adjacency matrix. This approach ensures that sensitive edge information is protected while still allowing for effective training of graph neural networks (GNNs). Additionally, Eclipse offers better resilience against common edge attacks such as LPA and LINKTELLER compared to DPGCN and LPGNet.
When it comes to model utility, Eclipse maintains good performance while providing robust privacy protection. It achieves a significantly better privacy-utility tradeoff compared to existing methods, showing gains in model utility by up to 46% under strong privacy constraints (𝜖 < 4). Even under extreme privacy constraints (𝜖 < 1), Eclipse still maintains higher model utility compared to training with node features only (MLP). Overall, Eclipse strikes a balance between protecting private edges and maintaining high model accuracy.

What are the potential implications of using low-rank decomposition for privacy preservation in GNNs

The use of low-rank decomposition for privacy preservation in GNNs has several potential implications:

Reduced Dimensionality: Low-rank decomposition reduces the dimensionality of the graph data by focusing on the most important components captured by the principal bases and singular values. This reduction can lead to more efficient computation and storage requirements.

Privacy Enhancement: By leveraging low-rank structures observed in real-world graphs, low-rank decomposition helps protect sensitive edge information while preserving essential topological features necessary for GNN training.

Improved Privacy-Utility Tradeoff: The compact representation obtained through low-rank decomposition allows for stronger differential privacy guarantees with minimal impact on model performance. This leads to a better balance between protecting user data and achieving accurate predictions.

Resilience Against Attacks: Low-rank decomposition can enhance resilience against adversarial attacks targeting graph structure reconstruction or inference tasks due to reduced exposure of detailed connectivity information.

Overall, incorporating low-rank decomposition into GNN training methodologies like Eclipse can offer enhanced privacy protection without compromising model effectiveness.

How can the concept of differential privacy be extended beyond edge-level protection in graph neural networks

Differential Privacy (DP) can be extended beyond edge-level protection in graph neural networks by considering additional levels of granularity within the data:

Node-Level DP: Extending DP protections at the node level involves ensuring that individual nodes' attributes are also safeguarded during training and inference processes within GNNs.

Graph-Level DP: Protecting entire subgraphs or communities within a larger network could involve applying DP mechanisms at a higher level than just individual edges or nodes.

Feature-Level DP: Ensuring that specific features used as input variables in GNN models are also subject to differential privacy measures can further enhance overall data protection.

By expanding differential privacy considerations beyond just edges in graph neural networks, comprehensive safeguards can be implemented across various dimensions of graph data analysis tasks for improved security and confidentiality measures.