面向去中心化双重差分隐私的谱图卷积神经网络

doi:10.3969/j.issn.1671-1122.2022.02.005

信息网络安全 ›› 2022, Vol. 22 ›› Issue (2): 39-46.doi: 10.3969/j.issn.1671-1122.2022.02.005

面向去中心化双重差分隐私的谱图卷积神经网络

刘峰¹^,², 杨成意²^,³, 於欣澄³, 齐佳音²()

1.华东师范大学计算机科学与技术学院,上海200062
2.上海对外经贸大学人工智能与变革管理研究院,上海 200336
3.上海对外经贸大学统计与信息学院,上海 201620

收稿日期:2021-08-06 出版日期:2022-02-10 发布日期:2022-02-16
通讯作者: 齐佳音 E-mail:ai@suibe.edu.cn
作者简介:刘峰（1988—）,男,湖北,博士研究生,主要研究方向为区块链、可计算情感|杨成意（1994—）,男,上海,博士研究生,主要研究方向为深度学习、区块链、网络安全|於欣澄（1996—）,女,浙江,硕士研究生,主要研究方向为深度学习、自然语言处理|齐佳音（1972—）,女,陕西,教授,博士,主要研究方向为先进技术和管理创新
基金资助:
国家重点研发计划(2017YFB0803304);国家自然科学基金(72042004)

Spectral Graph Convolutional Neural Network for Decentralized Dual Differential Privacy

LIU Feng¹^,², YANG Chengyi²^,³, YU Xincheng³, QI Jiayin²()

1. School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
2. Institute of Artificial Intelligence and Change Management, Shanghai University of International Business and Economics, Shanghai 200336, China
3. School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China

Received:2021-08-06 Online:2022-02-10 Published:2022-02-16
Contact: QI Jiayin E-mail:ai@suibe.edu.cn

摘要/Abstract

摘要：

图卷积神经网络是一种面向多任务且应用广泛的深度学习模型。文章研究了去中心化场景中谱域图卷积神经网络节点关系信息和节点特征信息的保护问题,提出双重差分隐私保护机制下的谱图卷积神经网络DDPSGCN。在给定隐私预算总额的条件下对拉普拉斯机制和高斯机制进行隐私预算分配,并通过隐私损失和Chernoff界理论进行参数估计。在两大分布噪声扰动作用基于不同图数据信息的隐私保护下,文章提出基于区块链去中心化差分隐私处理机制的图卷积神经网络训练算法。实验表明文章采用的去中心化双重差分隐私机制,能够在半监督节点分类任务准确率下降1%以内的前提下确保原始数据隐私不泄露,相较于单隐私保护机制有着更高的隐私保护效率和更强的对抗攻击鲁棒性。

关键词: 双重差分隐私, 去中心化差分隐私, 谱图卷积神经网络模型, 区块链

Abstract:

Graph convolution neural network is a multi-task oriented and widely-used deep learning model. This paper focused on the protection of node relationship information and node feature information of graph convolutional neural network in spectral domain for decentralized scenes, and proposed a spectral graph convolutional neural network based on dual differential privacy protection mechanism called DDPSGCN. Given the total amount of privacy budget, the Laplacian mechanism and Gaussian mechanism are allocated privacy budget, and the parameters of the two distributions are estimated by privacy loss and Chernoff bound theory. The paper proposed a graph convolution neural network training algorithm based on block chain decentralized differential privacy processing mechanism under the influence of two kinds of distributed noise. Experiments show that the decentralized dual differential privacy mechanism can ensure the privacy of the original data without leakage under the premise that the accuracy of semi-supervised node classification task is reduced by less than 1%,which has higher privacy protection efficiency and stronger robustness against attacks compared with the single privacy protection mechanism.

Key words: dual differential privacy, decentralized differential privacy, spectral graph convolutional neural network, blockchain

中图分类号:

TP309

刘峰, 杨成意, 於欣澄, 齐佳音. 面向去中心化双重差分隐私的谱图卷积神经网络[J]. 信息网络安全, 2022, 22(2): 39-46.

LIU Feng, YANG Chengyi, YU Xincheng, QI Jiayin. Spectral Graph Convolutional Neural Network for Decentralized Dual Differential Privacy[J]. Netinfo Security, 2022, 22(2): 39-46.

图/表 3

参考文献 17

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面向去中心化双重差分隐私的谱图卷积神经网络

Spectral Graph Convolutional Neural Network for Decentralized Dual Differential Privacy

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