基于函数加密策略的深度学习窃电检测模型

DEEP LEARNING ELECTRICITY THEFT DETECTION MODEL BASED ON FUNCTIONAL ENCRYPTION STRATEGY

  • 摘要: 为了实现有效的隐私保护以及高效精确的窃电检测,提出一种基于函数加密策略的深度学习窃电检测模型。每个用户使用函数加密对功能被数据进行加密,运营商使用函数解密密钥计算动态账单,并且实现电网负荷监控。进一步提出一个基于深度学习的窃电检测模型,并利用函数加密支持的加密数据进行内积计算。仿真结果表明,该方法能够准确地检测窃电用户,并在可接受的通信和计算开销下保护用户的隐私。

     

    Abstract: To achieve effective privacy protection and efficient electricity theft detection, this paper proposes a deep learning electricity theft detection model based on functional encryption. Each user encrypted power consumption readings via functional encryption, while operators utilized decryption keys to compute dynamic bills and monitor grid load. A deep learning model was designed to detect electricity theft by performing inner product operations on encrypted data. Simulation results demonstrate the model’s accuracy in identifying fraudulent users while maintaining privacy with acceptable communication and computational overhead.

     

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