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The detection of energy thefts is vital for the safety of the whole smartgrid system. However, the detection alone is not enough since energy thefts cancrucially affect the electricity supply leading to some blackouts. Moreover,privacy is one of the major challenges that must be preserved when dealing withclients' energy data. This is often overlooked in energy theft detectionresearch 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 effectivedemand management that employs two layers of privacy protection. We explore asplit learning mechanism that trains a detection model in a decentralisedfashion without the need to exchange raw data. We also employ a second layer ofprivacy by the use of a masking scheme to mask clients' outputs in order toprevent inference attacks. A privacy-enhanced version of this mechanism alsoemploys an additional layer of privacy protection by training a randomisationlayer at the end of the client-side model. This is done to make the output asrandom as possible without compromising the detection performance. For theenergy theft detection part, we design a multi-output machine learning model toidentify energy thefts, estimate their volume, and effectively predict futuredemand. Finally, we use a comprehensive set of experiments to test our proposedscheme. The experimental results show that our scheme achieves high detectionaccuracy and greatly improves the privacy preservation degree.

A Privacy-Preserving Energy Theft Detection Model for Effective Demand-Response Management in Smart Grids