Edge computing privacy protection method based on blockchain and federated learning

Chen FANG; Yuanbo GUO; Yifeng WANG; Yongjin HU; Jiali MA; Han ZHANG; Yangyang HU

doi:10.11959/j.issn.1000-436x.2021190

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Edge computing privacy protection method based on blockchain and federated learning

Topics: New Technology of Computer Communication and Network System Security | 更新时间：2024-06-05

- Edge computing privacy protection method based on blockchain and federated learning
- Journal on Communications Vol. 42, Issue 11, Pages: 28-40(2021)
- 作者机构：
  
  1. 信息工程大学密码工程学院，河南郑州 450001
  2. 郑州大学网络空间安全学院，河南郑州 450003
  3. 加利福尼亚大学河滨分校，河滨 CA92521
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61501515)
- DOI：10.11959/j.issn.1000-436x.2021190
  CLC： TP301
- Online First：2021-11，
  
  Published：25 November 2021
- 稿件说明：
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Chen FANG, Yuanbo GUO, Yifeng WANG, et al. Edge computing privacy protection method based on blockchain and federated learning[J]. Journal on Communications, 2021, 42(11): 28-40.
DOI：

Chen FANG, Yuanbo GUO, Yifeng WANG, et al. Edge computing privacy protection method based on blockchain and federated learning[J]. Journal on Communications, 2021, 42(11): 28-40. DOI： 10.11959/j.issn.1000-436x.2021190.

摘要

针对边缘计算的数据隐私性、计算结果正确性和数据处理过程可审计性等需求，提出了一种基于区块链和联邦学习的边缘计算隐私保护方法，不需要可信环境和特殊硬件设施即可在网络边缘处联合多设备实现安全可靠的协同训练。利用区块链赋予边缘计算防篡改和抗单点故障攻击等特性，并在共识协议中融入梯度验证和激励机制，鼓励更多的本地设备诚实地向联邦学习贡献算力和数据。对于联邦学习共享模型参数导致的潜在隐私泄露问题，设计自适应差分隐私机制保护参数隐私的同时减小噪声对模型准确性的影响，并通过时刻统计精确追踪训练过程中的隐私损失。实验结果表明，所提方法能够抵抗30%的中毒攻击，并且能以较高的模型准确率实现隐私保护，适用于对安全性和准确性要求较高的边缘计算场景。

Abstract

Aiming at the needs of edge computing for data privacy

the correctness of calculation results and the auditability of data processing

a privacy protection method for edge computing based on blockchain and federated learning was proposed

which can realize collaborative training with multiple devices at the edge of the network without a trusted environment and special hardware facilities.The blockchain was used to endow the edge computing with features such as tamper-proof and resistance to single-point-of-failure attacks

and the gradient verification and incentive mechanism were incorporated into the consensus protocol to encourage more local devices to honestly contribute computing power and data to the federated learning.For the potential privacy leakage problems caused by sharing model parameters

an adaptive differential privacy mechanism was designed to protect parameter privacy while reducing the impact of noise on the model accuracy

and moments accountant was used to accurately track the privacy loss during the training process.Experimental results show that the proposed method can resist 30% of poisoning attacks

and can achieve privacy protection with high model accuracy

and is suitable for edge computing scenarios that require high level of security and accuracy.

关键词

Keywords

references

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