In advanced metering infrastructure (AMI), smart meters (SMs) are installed at the consumer side to send fine-grained power consumption readings periodically to the system operator (SO) for load monitoring, energy management, and billing. However, fraudulent consumers launch electricity theft cyber attacks by reporting false readings to reduce their bills illegally. These attacks do not only cause financial losses but may also degrade the grid performance because the readings are used for grid management. To identify these attackers, the existing schemes employ machine-learning models using the consumers’ fine-grained readings, which violates the consumers’ privacy by revealing their lifestyle. In this article, we propose an efficient scheme that enables the SO to detect electricity theft, compute bills, and monitor load while preserving the consumers’ privacy. The idea is that SMs encrypt their readings using functional encryption (FE), and the SO uses the ciphertexts to: 1) compute the bills following the dynamic pricing approach; 2) monitor the grid load; and 3) evaluate a machine-learning model to detect fraudulent consumers, without being able to learn the individual readings to preserve consumers’ privacy. We adapted an FE scheme so that the encrypted readings are aggregated for billing and load monitoring and only the aggregated value is revealed to the SO. Also, we exploited the inner-product operations on encrypted readings to evaluate a machine-learning model to detect fraudulent consumers. The real data set is used to evaluate our scheme, and our evaluations indicate that our scheme is secure and can detect fraudulent consumers accurately with low communication and computation overhead.

中文翻译：

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带有动态计费和AMI网络负载监控的高效保护隐私的电力盗窃检测

在高级计量基础架构（AMI）中，在用户端安装了智能电表（SM），以将细粒度的功耗读数定期发送给系统运营商（SO），以进行负载监视，能源管理和计费。但是，欺诈的消费者通过报告错误的读数来非法窃取他们的账单，从而发动了电力盗窃网络攻击。这些读数不仅造成财务损失，而且还可能降低电网性能，因为读数用于电网管理。为了识别这些攻击者，现有的方案采用了基于消费者细粒度读数的机器学习模型，该模型通过揭示消费者的生活方式来侵犯消费者的隐私。在本文中，我们提出了一种有效的方案，使SO能够检测到盗窃电，计算账单，并监控负载，同时保护消费者的隐私。这个想法是，SM使用功能加密（FE）来加密其读数，而SO使用密文来：1）按照动态定价方法计算账单；2）监控电网负荷；和3）评估机器学习模型以检测欺诈的消费者，而无法学习个人阅读以保护消费者的隐私。我们调整了FE方案，以便将加密的读数汇总以用于计费和负载监控，并且仅将汇总的值显示给SO。此外，我们利用对加密读数的内部产品操作来评估机器学习模型，以检测欺诈性消费者。实际数据集用于评估我们的方案，