Journal on Communications ›› 2021, Vol. 42 ›› Issue (11): 28-40.doi: 10.11959/j.issn.1000-436x.2021190

Special Issue: 边缘计算 联邦学习 区块链

• Topics: New Technology of Computer Communication and Network System Security • Previous Articles     Next Articles

Edge computing privacy protection method based on blockchain and federated learning

Chen FANG1, Yuanbo GUO1, Yifeng WANG1, Yongjin HU1, Jiali MA1, Han ZHANG2, Yangyang HU3   

  1. 1 Department of Cryptogram Engineering, Information Engineering University, Zhengzhou 450001, China
    2 School of Cyberspace Security, Zhengzhou University, Zhengzhou 450003, China
    3 University of California, Riverside, Riverside CA92521, USA
  • Revised:2021-09-15 Online:2021-11-25 Published:2021-11-01
  • Supported by:
    The National Natural Science Foundation of China(61501515)

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.

Key words: federated learning, edge computing, blockchain, poisoning attack, privacy preservation

CLC Number: 

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