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

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