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Locally Private Graph Neural Networks (2006.05535v9)

Published 9 Jun 2020 in cs.LG, cs.CR, and stat.ML

Abstract: Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non-relational data, there is less work addressing the privacy issues pertained to applying deep learning algorithms on graphs. In this paper, we study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private, but they could be beneficial for a central server for training a GNN over the graph. To address this problem, we develop a privacy-preserving, architecture-agnostic GNN learning algorithm with formal privacy guarantees based on Local Differential Privacy (LDP). Specifically, we propose an LDP encoder and an unbiased rectifier, by which the server can communicate with the graph nodes to privately collect their data and approximate the GNN's first layer. To further reduce the effect of the injected noise, we propose to prepend a simple graph convolution layer, called KProp, which is based on the multi-hop aggregation of the nodes' features acting as a denoising mechanism. Finally, we propose a robust training framework, in which we benefit from KProp's denoising capability to increase the accuracy of inference in the presence of noisy labels. Extensive experiments conducted over real-world datasets demonstrate that our method can maintain a satisfying level of accuracy with low privacy loss.

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Authors (2)
  1. Sina Sajadmanesh (9 papers)
  2. Daniel Gatica-Perez (24 papers)
Citations (109)

Summary

  • The paper introduces a multi-bit mechanism (MBM) that efficiently handles high-dimensional node features while preserving local privacy.
  • It proposes KProp to approximate graph convolution by aggregating higher-order neighbor data, addressing limited neighborhood sizes.
  • The study presents Drop, a robust label denoising algorithm that leverages graph structure to enhance inference accuracy under noise.

Locally Private Graph Neural Networks: A Review

In the evolving landscape of graph neural networks (GNNs), the intersection of graph-based learning and privacy preservation represents a crucial research frontier. The paper "Locally Private Graph Neural Networks" by Sajadmanesh and Gatica-Perez addresses this intersection by proposing a novel method for privacy-preserving learning in GNNs, focusing specifically on the protection of node-level data using Local Differential Privacy (LDP).

Summary of Contributions

The paper presents a framework that allows a central server to efficiently train a GNN while preserving the privacy of node data, specifically the features and labels associated with the nodes on the graph. The core contributions of the research are as follows:

  1. Multi-Bit Mechanism (MBM): The authors propose a novel multi-bit mechanism designed to preserve the privacy of high-dimensional node features. This method, built upon the 1-bit mechanism, is designed to efficiently handle multi-dimensional data by encoding perturbed feature vectors using LDP. This mechanism aims to minimize communication overhead between the nodes and the server, ensuring feasible deployment in real-world applications.
  2. Graph Convolution Approximation via KProp: To enhance the utility of the privacy-preserved data, the authors introduce KProp. This is a simple yet effective mechanism that extends neighborhood aggregation to higher-order neighbors, effectively mitigating the small neighborhood size of nodes, especially those in power-law distributed graphs. By integrating the KProp layer before the actual GNN layers, the framework can approximate the first layer of graph convolution more accurately in the presence of noisy data.
  3. Label Denoising with Propagation (Drop): For the protection of node labels, the authors employ generalized randomized response and propose a robust learning algorithm called Drop. This algorithm explicitly handles label noise, which is crucial in maintaining the accuracy of inferences made by the GNN. By effectively leveraging the graph structure, Drop aims to successfully denoise and propagate the perturbed labels, significantly enhancing the model’s robustness.

Strong Numerical Results and Implications

The experimental results reported demonstrate the efficacy of the proposed methods across various datasets. For instance, even with low privacy budgets, the approach maintains a satisfying level of accuracy compared to non-private implementations. This is particularly impressive given the trade-offs typically associated with privacy-preserving mechanisms.

The paper's approach provides formal privacy guarantees while ensuring that the inherent structural information within the graph is adequately utilized. This balance has significant implications for applications requiring strict adherence to privacy standards, such as social network analysis and mobile applications, where personal and sensitive data are prevalent.

Practical and Theoretical Implications

The framework introduced in this paper has several practical implications:

  • Applicability in Real-World Settings: The architecture-agnostic nature of the proposed GNN learning algorithm means that it can be easily integrated into existing GNN architectures with minimal adjustments. This flexibility is crucial for broad adoption in privacy-sensitive industries.
  • A Step Toward Federated Learning Paradigms: By ensuring data remains local with users, this approach aligns with federated learning paradigms, enhancing data privacy and compliance with regulations like GDPR, without compromising too heavily on the model's performance.
  • Extensibility to Other Privacy Concerns: Although the paper primarily addresses node data privacy, the methods and findings could potentially be extended to cover additional privacy concerns such as edge privacy, or even attribute inference protection, paving the way for comprehensive privacy-preserving graph learning systems.

Future Directions

Future work could explore the integration of this LDP-based approach with other privacy frameworks, such as differential privacy at the server level, to enhance protection. Additionally, optimization of the trade-off between neighborhood size in KProp and over-smoothing could yield further enhancements in model accuracy. Exploring automated tuning of privacy budgets per feature, informed by individual feature sensitivity, could also be an exciting avenue.

In conclusion, this paper represents a significant contribution to the field of privacy-preserving machine learning for graph data, offering a viable pathway towards more secure and privacy-conscious graph-based inference systems.

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