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

Pan, Q, Wu, J, Bashir, AK, Li, J, Yang, W and Al-Otaibi, YD (2022) Joint Protection of Energy Security and Information Privacy for Energy Harvesting: An Incentive Federated Learning Approach. IEEE Transactions on Industrial Informatics, 18 (5). pp. 3473-3483. ISSN 1551-3203

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Abstract

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 above challenge, this paper 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. Secondly, a differential privacy-empowered information preservation scheme is devised, where sensitive information is perturbed and protected by the customized demand-based noise. Thirdly, a non-cooperative 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.