The detection of energy thefts is vital for the safety of the whole smart
grid system. However, the detection alone is not enough since energy thefts can
crucially affect the electricity supply leading to some blackouts. Moreover,
privacy is one of the major challenges that must be preserved when dealing with
clients’ energy data. This is often overlooked in energy theft detection
research as most current detection techniques rely on raw, unencrypted data,
which may potentially expose sensitive and personal data. To solve this issue,
we present a privacy-preserving energy theft detection technique with effective
demand management that employs two layers of privacy protection. We explore a
split learning mechanism that trains a detection model in a decentralised
fashion without the need to exchange raw data. We also employ a second layer of
privacy by the use of a masking scheme to mask clients’ outputs in order to
prevent inference attacks. A privacy-enhanced version of this mechanism also
employs an additional layer of privacy protection by training a randomisation
layer at the end of the client-side model. This is done to make the output as
random as possible without compromising the detection performance. For the
energy theft detection part, we design a multi-output machine learning model to
identify energy thefts, estimate their volume, and effectively predict future
demand. Finally, we use a comprehensive set of experiments to test our proposed
scheme. The experimental results show that our scheme achieves high detection
accuracy and greatly improves the privacy preservation degree.

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