Efficient Privacy-Preserving Electricity Theft Detection with Dynamic Billing and Load Monitoring for AMI Networks
This addresses privacy concerns and financial losses in advanced metering infrastructure for utility operators and consumers, though it is incremental as it builds on existing functional encryption and machine-learning methods.
The paper tackles the problem of electricity theft in smart grids by proposing a privacy-preserving scheme that uses functional encryption to allow system operators to compute bills, monitor load, and detect fraudulent consumers without accessing individual power consumption readings, achieving accurate detection with low overhead as validated on a real dataset.
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, billing, etc. 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 paper, 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, and the SO uses the ciphertexts to (i) compute the bills following dynamic pricing approach, (ii) monitor the grid load, and (iii) evaluate a machine-learning model to detect fraudulent consumers, without being able to learn the individual readings to preserve consumers' privacy. We adapted a functional encryption 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. Real dataset 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.