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In advanced metering infrastructure (AMI), smart meters (SMs) are installedat the consumer side to send fine-grained power consumption readingsperiodically to the system operator (SO) for load monitoring, energymanagement, billing, etc. However, fraudulent consumers launch electricitytheft cyber-attacks by reporting false readings to reduce their billsillegally. These attacks do not only cause financial losses but may alsodegrade the grid performance because the readings are used for grid management.To identify these attackers, the existing schemes employ machine-learningmodels using the consumers' fine-grained readings, which violates theconsumers' privacy by revealing their lifestyle. In this paper, we propose anefficient scheme that enables the SO to detect electricity theft, computebills, and monitor load while preserving the consumers' privacy. The idea isthat SMs encrypt their readings using functional encryption, and the SO usesthe ciphertexts to (i) compute the bills following dynamic pricing approach,(ii) monitor the grid load, and (iii) evaluate a machine-learning model todetect fraudulent consumers, without being able to learn the individualreadings to preserve consumers' privacy. We adapted a functional encryptionscheme so that the encrypted readings are aggregated for billing and loadmonitoring and only the aggregated value is revealed to the SO. Also, weexploited the inner-product operations on encrypted readings to evaluate amachine-learning model to detect fraudulent consumers. Real dataset is used toevaluate our scheme, and our evaluations indicate that our scheme is secure andcan detect fraudulent consumers accurately with low communication andcomputation overhead.

Efficient Privacy-Preserving Electricity Theft Detection with Dynamic Billing and Load Monitoring for AMI Networks