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.

翻译：窃电检测对于整个智能电网系统的安全至关重要。然而，仅靠检测并不足够，因为窃电可能严重影响电力供应，甚至导致停电。此外，隐私是处理客户能源数据时必须维护的重大挑战之一。这一需求在窃电检测研究中常被忽视，因为当前大多数检测技术依赖原始未加密数据，这可能会暴露敏感的个人信息。为解决这一问题，本文提出了一种结合高效需求管理的隐私保护型窃电检测技术，该技术采用两层隐私保护机制。我们探索了一种分割学习机制，以去中心化方式训练检测模型，无需交换原始数据。我们还通过掩码方案对客户输出进行遮蔽，作为第二层隐私保护，以防止推理攻击。该机制的隐私增强版本在客户端模型末端训练一个随机化层，以此增加额外隐私保护层。这一设计的目的是在不牺牲检测性能的前提下，使输出尽可能随机化。在窃电检测部分，我们设计了一个多输出机器学习模型，用于识别窃电行为、估算其规模并有效预测未来需求。最后，我们通过一系列综合实验验证了所提方案。实验结果表明，该方案在实现高检测精度的同时，显著提升了隐私保护程度。