基于区块链和联邦学习的边缘计算隐私保护方法

方晨; 郭渊博; 王一丰; 胡永进; 马佳利; 张晗; 胡阳阳

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

您当前的位置：

首页 >

文章列表页 >

基于区块链和联邦学习的边缘计算隐私保护方法

专题：计算机通信与网络系统安全技术 | 更新时间：2024-06-05

- 基于区块链和联邦学习的边缘计算隐私保护方法
- Edge computing privacy protection method based on blockchain and federated learning
- 通信学报 2021年42卷第11期页码：28-40
- 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：
  
  [ "方晨（1993− ），男，安徽宿松人，信息工程大学博士生，主要研究方向为机器学习隐私安全" ]
  [ "郭渊博（1975− ），男，陕西周至人，博士，信息工程大学教授、博士生导师，主要研究方向为大数据安全、态势感知" ]
  [ "王一丰（1994− ），男，江苏泰兴人，信息工程大学博士生，主要研究方向为深度学习、网络安全" ]
  [ "胡永进（1981− ），男，山东潍坊人，信息工程大学讲师，主要研究方向为大数据安全、态势感知" ]
  [ "马佳利（1996− ），男，河北邢台人，信息工程大学博士生，主要研究方向为数字孪生、机器学习" ]
  [ "张晗（1985− ），女，河南项城人，郑州大学讲师，主要研究方向为机器学习、自然语言处理" ]
  [ "胡阳阳（1990− ），男，江苏南京人，加利福尼亚大学河滨分校博士生，主要研究方向为机器学习" ]
- 基金信息：
  
  国家自然科学基金资助项目(61501515)
- DOI：10.11959/j.issn.1000-436x.2021190
  中图分类号： TP301
- 网络出版日期：2021-11，
  
  纸质出版日期：2021-11-25
- 稿件说明：
移动端阅览
方晨, 郭渊博, 王一丰, 等. 基于区块链和联邦学习的边缘计算隐私保护方法[J]. 通信学报, 2021,42(11):28-40.

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.
方晨, 郭渊博, 王一丰, 等. 基于区块链和联邦学习的边缘计算隐私保护方法[J]. 通信学报, 2021,42(11):28-40. DOI： 10.11959/j.issn.1000-436x.2021190.

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

周俊 , 沈华杰 , 林中允 , 等 . 边缘计算隐私保护研究进展 [J ] . 计算机研究与发展 , 2020 , 57 ( 10 ): 2027 - 2051 .

ZHOU J , SHEN H J , LIN Z Y , et al . Research advances on privacy preserving in edge computing [J ] . Journal of Computer Research and Development , 2020 , 57 ( 10 ): 2027 - 2051 .

MCMAHAN H B , MOORE E , RAMAGE D , et al . Communication-efficient learning of deep networks from decentralized data [J ] . arXiv Preprint,arXiv:1602.05629 , 2016 .

曾诗钦 , 霍如 , 黄韬 , 等 . 区块链技术研究综述：原理、进展与应用 [J ] . 通信学报 , 2020 , 41 ( 1 ): 134 - 151 .

ZENG S Q , HUO R , HUANG T , et al . Survey of blockchain:principle,progress and application [J ] . Journal on Communications , 2020 , 41 ( 1 ): 134 - 151 .

KIM H , PARK J , BENNIS M , et al . Blockchained on-device federated learning [J ] . IEEE Communications Letters , 2020 , 24 ( 6 ): 1279 - 1283 .

QU Y Y , POKHREL S R , GARG S , et al . A blockchained federated learning framework for cognitive computing in industry 4.0 networks [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 4 ): 2964 - 2973 .

WANG Q L , GUO Y F , WANG X F , et al . AI at the edge:blockchain-empowered secure multiparty learning with heterogeneous models [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 10 ): 9600 - 9610 .

LU Y L , HUANG X H , ZHANG K , et al . Blockchain empowered asynchronous federated learning for secure data sharing in Internet of vehicles [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 4 ): 4298 - 4311 .

QU Y Y , GAO L X , LUAN T H , et al . Decentralized privacy using blockchain-enabled federated learning in fog computing [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 6 ): 5171 - 5183 .

ZHAO Y , ZHAO J , JIANG L S , et al . Privacy-preserving blockchain-based federated learning for IoT devices [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 3 ): 1817 - 1829 .

LU Y L , HUANG X H , DAI Y Y , et al . Blockchain and federated learning for privacy-preserved data sharing in industrial IoT [J ] . IEEE Transactions on Industrial Informatics , 2020 , 16 ( 6 ): 4177 - 4186 .

QI Y H , HOSSAIN M S , NIE J T , et al . Privacy-preserving blockchain-based federated learning for traffic flow prediction [J ] . Future Generation Computer Systems , 2021 , 117 : 328 - 337 .

LIU Y , PENG J L , KANG J W , et al . A secure federated learning framework for 5G networks [J ] . IEEE Wireless Communications , 2020 , 27 ( 4 ): 24 - 31 .

SHORT A R , LELIGOU H C , PAPOUTSIDAKIS M , et al . Using blockchain technologies to improve security in federated learning systems [C ] // Proceedings of 2020 IEEE 44th Annual Computers,Software,and Applications Conference (COMPSAC) . Piscataway:IEEE Press , 2020 : 1183 - 1188 .

GILAD Y , HEMO R , MICALI S , et al . Algorand:scaling Byzantine agreements for cryptocurrencies [C ] // Proceedings of the 26th Symposium on Operating Systems Principles . New York:ACM Press , 2017 : 51 - 68 .

DWORK C , ROTH A . The algorithmic foundations of differential privacy [J ] . Foundations and Trends® in Theoretical Computer Science , 2013 , 9 ( 3/4 ): 211 - 407 .

FREDRIKSON M , JHA S , RISTENPART T . Model inversion attacks that exploit confidence information and basic countermeasures [C ] // Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . New York:ACM Press , 2015 : 1322 - 1333 .

CHEN L J , KOUTRIS P , KUMAR A . Model-based pricing for machine learning in a data marketplace [J ] . arXiv Preprint,arXiv:1805.11450 , 2018 .

KURTULMUS A B , DANIEL K . Trustless machine learning contracts;evaluating and exchanging machine learning models on the ethereum blockchain [J ] . arXiv Preprint,arXiv:1802.10185 , 2018 .

LI C L , FU Y C , YU F R , et al . Vehicle position correction:a vehicular blockchain networks-based GPS error sharing framework [J ] . IEEE Transactions on Intelligent Transportation Systems , 2020 , 22 ( 2 ): 898 - 912 .

CHEN L , XU L , SHAH N , et al . On security analysis of proof-ofelapsed-time (PoET) [C ] // Proceedings of International Symposium on Stabilization,Safety,and Security of Distributed Systems . Berlin:Springer International Publishing , 2017 : 282 - 297 .

WENG J S , WENG J , ZHANG J L , et al . DeepChain:auditable and privacy-preserving deep learning with blockchain-based incentive [J ] . IEEE Transactions on Dependable and Secure Computing , 2021 , 18 ( 5 ): 2438 - 2455 .

SHAYAN M , FUNG C , YOON C J M , et al . Biscotti:a ledger for private and secure peer-to-peer machine learning [J ] . arXiv Preprint,arXiv:1811.09904 , 2018 .

ABADI M , CHU A , GOODFELLOW I , et al . Deep learning with differential privacy [C ] // Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . New York:ACM Press , 2016 : 308 - 318 .

GEYER R C , KLEIN T , NABI M . Differentially private federated learning:a client level perspective [J ] . arXiv Preprint,arXiv:1712.07557 , 2017 .

FANG C , GUO Y B , HU Y J , et al . Privacy-preserving and communication-efficient federated learning in Internet of Things [J ] . Computers＆ Security , 2021 , 103 : 102199 .

XU C G , REN J , ZHANG D Y , et al . GANobfuscator:mitigating information leakage under GAN via differential privacy [J ] . IEEE Transactions on Information Forensics and Security , 2019 , 14 ( 9 ): 2358 - 2371 .

BLANCHARD P , MHAMDI E M E , GUERRAOUI R , et al . Machine learning with adversaries:byzantine tolerant gradient descent [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems.New York:Curran Associates Inc. . 2017 : 118 - 128 .

DEMERS A , GREENE D , HAUSER C , et al . Epidemic algorithms for replicated database maintenance [C ] // Proceedings of the 6th annual ACM Symposium on Principles of distributed computing . New York:ACM Press , 1987 : 1 - 12 .

HUANG L , JOSEPH A D , NELSON B , et al . Adversarial machine learning [C ] // Proceedings of the 4th ACM workshop on Security and artificial intelligence . New York:ACM Press , 2011 : 43 - 58 .

浏览量

2449

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于多级代理许可区块链的联邦边缘学习模型

面向车联网数据隐私保护的高效分布式模型共享策略

基于自适应拜占庭防御的安全联邦学习方案

异构边缘计算环境下异步联邦学习的节点分组与分时调度策略

基于区块链的计算与无线通信资源联合管理双向拍卖模型