洞察 - Machine Learning - # Privacy-Preserving Graph Neural Networks

Decoupled Graph Neural Networks with Node-Level Differential Privacy

Q: How can the DPAR framework be adapted for different types of graph datasets?

The DPAR framework can be adapted for different types of graph datasets by adjusting the hyperparameters and algorithms used in the training process. For instance, the sampling rate, batch size, learning rate, and privacy budget allocation can be tuned based on the characteristics of each dataset. Additionally, the choice between using DP-APPR with exponential mechanism or Gaussian mechanism can be tailored to suit specific dataset requirements. The sparsity level (퐾) chosen for computing top-퐾 neighbors from APPR vectors can also be adjusted based on the density and connectivity patterns of the graph data.

Q: What are the potential limitations or drawbacks of using DP-SGD for feature aggregation in GNN training?

Using DP-SGD for feature aggregation in GNN training may have some limitations and drawbacks: Privacy-Utility Trade-off: Adding noise to gradients during feature aggregation to achieve differential privacy may lead to a trade-off between privacy protection and model accuracy. The amount of noise added could impact model performance. Sensitivity Issues: In complex graphs with high connectivity, nodes' features might have strong correlations that increase sensitivity when calculating gradients, potentially requiring higher levels of noise. Computational Overhead: Implementing DP-SGD for feature aggregation could introduce additional computational overhead due to noise addition and gradient clipping operations. Complexity: Managing privacy guarantees while ensuring effective feature aggregation through DP-SGD requires careful parameter tuning and algorithm design.

Q: How might the concept of node-level differential privacy impact broader applications beyond graph neural networks?

Node-level differential privacy has implications beyond graph neural networks in various applications: Healthcare Data: Protecting individual patient records within a healthcare network while allowing collaborative analysis among institutions. Financial Transactions: Ensuring sensitive financial information remains private while enabling secure data sharing among financial institutions. Social Networks: Safeguarding user profiles and interactions on social media platforms to prevent unauthorized access or misuse of personal data. IoT Networks: Securing data exchanged between Internet-of-Things devices without compromising individual device identities or usage patterns. By applying node-level differential privacy principles across these diverse domains, organizations can maintain data confidentiality while fostering collaboration and innovation in their respective fields.

核心概念

Achieving node-level differential privacy for training GNNs with enhanced privacy-utility trade-off.

摘要

Graph Neural Networks (GNNs) have shown remarkable performance in analyzing graph-structured data. This paper introduces a novel approach, DPAR, that focuses on achieving node-level differential privacy for GNN training. By decoupling feature aggregation and message passing, DPAR enhances the privacy-utility trade-off compared to existing methods. The proposed algorithms outperform state-of-the-art techniques like GAP and SAGE in terms of test accuracy under the same privacy budget across various datasets. The study highlights the importance of balancing privacy protection for both node features and graph structures in GNN training.

自定义摘要

使用 AI 改写

生成参考文献

翻译原文

翻译成其他语言

生成思维导图

从原文生成

访问来源

arxiv.org

统计

Privacy budget: 휖 = 1, Test accuracy: 0.3421 (Cora-ML)
Privacy budget: 휖 = 1, Test accuracy: 0.8569 (MS Academic)
Privacy budget: 휖 = 1, Test accuracy: 0.8927 (CS)
Privacy budget: 휖 = 1, Test accuracy: 0.934 (Reddit)
Privacy budget: 휖 = 1, Test accuracy: 0.8948 (Physics)

引用

"Our framework achieves enhanced privacy-utility trade-off compared to existing layer-wise perturbation based methods."
"We propose a Decoupled GNN with Diﬀerentially Private Approximate Personalized PageRank (DPAR) for training GNNs with an enhanced privacy-utility tradeoﬀ."

从中提取的关键见解

DPAR

by Qiuchen Zhan... 在 arxiv.org 03-15-2024

https://arxiv.org/pdf/2210.04442.pdf

更深入的查询

How can the DPAR framework be adapted for different types of graph datasets?

The DPAR framework can be adapted for different types of graph datasets by adjusting the hyperparameters and algorithms used in the training process. For instance, the sampling rate, batch size, learning rate, and privacy budget allocation can be tuned based on the characteristics of each dataset. Additionally, the choice between using DP-APPR with exponential mechanism or Gaussian mechanism can be tailored to suit specific dataset requirements. The sparsity level (퐾) chosen for computing top-퐾 neighbors from APPR vectors can also be adjusted based on the density and connectivity patterns of the graph data.

What are the potential limitations or drawbacks of using DP-SGD for feature aggregation in GNN training?

Using DP-SGD for feature aggregation in GNN training may have some limitations and drawbacks:

Privacy-Utility Trade-off: Adding noise to gradients during feature aggregation to achieve differential privacy may lead to a trade-off between privacy protection and model accuracy. The amount of noise added could impact model performance.
Sensitivity Issues: In complex graphs with high connectivity, nodes' features might have strong correlations that increase sensitivity when calculating gradients, potentially requiring higher levels of noise.
Computational Overhead: Implementing DP-SGD for feature aggregation could introduce additional computational overhead due to noise addition and gradient clipping operations.
Complexity: Managing privacy guarantees while ensuring effective feature aggregation through DP-SGD requires careful parameter tuning and algorithm design.

How might the concept of node-level differential privacy impact broader applications beyond graph neural networks?

Node-level differential privacy has implications beyond graph neural networks in various applications:

Healthcare Data: Protecting individual patient records within a healthcare network while allowing collaborative analysis among institutions.
Financial Transactions: Ensuring sensitive financial information remains private while enabling secure data sharing among financial institutions.
Social Networks: Safeguarding user profiles and interactions on social media platforms to prevent unauthorized access or misuse of personal data.
IoT Networks: Securing data exchanged between Internet-of-Things devices without compromising individual device identities or usage patterns.

By applying node-level differential privacy principles across these diverse domains, organizations can maintain data confidentiality while fostering collaboration and innovation in their respective fields.