In advanced metering infrastructure (AMI), smart
meters (SMs) are installed at the consumer side to send finegrained 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.