Joint Protection of Energy Security and Information Privacy for Energy Harvesting: An Incentive Federated Learning Approach

Qianqian Pan, Jun Wu*, Ali Kashif Bashir, Jianhua Li, Wu Yang, Yasser D. Al-Otaibi

*この研究の対応する著者

研究成果: Article査読

7 被引用数 (Scopus)

抄録

Energy harvesting (EH) is a promising and critical technology to mitigate the dilemma between the limited battery capacity and the increasing energy consumption in the Internet of everything. However, the current EH system suffers from energy-information cross threats, facing the overlapping vulnerability of energy deprivation and private information leakage. Although some existing works touch on the security of energy and information in EH, they treat these two issues independently, without collaborative and intelligent protection cross the energy side and information side. To address the aforementioned challenge, this article proposes a joint protection framework of energy security and information privacy for EH with an incentive federated learning approach. First, we design a federated-learning-based malicious energy user detection method according to energy status and behaviors to provide energy security protection. Second, a differential-privacy-empowered information preservation scheme is devised, where sensitive information is perturbed and protected by the customized demand-based noise. Third, a noncooperative-game-enabled incentive mechanism is established to encourage EH nodes to participate in the joint energy-information protection system. The proposed incentive mechanism derives the optimal energy-information security strategy for EH nodes and achieve a tradeoff between the protection of energy security and information privacy. Evaluation results have verified the effectiveness of our proposed joint protection mechanism.

本文言語English
ページ(範囲)3473-3483
ページ数11
ジャーナルIEEE Transactions on Industrial Informatics
18
5
DOI
出版ステータスPublished - 2022 5月 1
外部発表はい

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 情報システム
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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