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<p>One of the significant challenges in Smart Grids (SG) is maintaining user privacy while maximizing data utility and ensuring proper billing. Sharing user data can expose user habits and behaviors, making privacy preservation a crucial aspect. Privacy-Preserving Data Aggregation (PPDA) serves as a fundamental component in preserving user privacy and managing demand-response (or load) in SG. However, existing PPDA schemes have limitations such as overlooking consumer billing, high computational costs, or reliance on weak security models. Furthermore, blockchain based solutions often suffer from high costs and inefficiency in data storage. To address these issues, this paper proposes a novel scheme called the computationally efficient, secure, privacy-preserving Data Aggregation for Load Monitoring and Billing Scheme (DALBS). DALBS leverages blockchain technology and Homomorphic Encryption techniques to fulfill diverse security requirements while ensuring maximum utility and accuracy for billing in SG. Through a comprehensive comparative analysis with existing state-of-the-art works, it is demonstrated that DALBS successfully meets privacy and security requirements, and ensures data utility and efficient billing, while minimizing computational and communication costs.</p>

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Blockchain and homomorphic-based data aggregation for load monitoring and billing in smart grids

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月20日

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  • Noshaba Naeem,
  • Fawad Khan,
  • Shahzaib Tahir,
  • Imran Makhdoom,
  • Muhammad Tariq,
  • Syed Aziz Shah

摘要

One of the significant challenges in Smart Grids (SG) is maintaining user privacy while maximizing data utility and ensuring proper billing. Sharing user data can expose user habits and behaviors, making privacy preservation a crucial aspect. Privacy-Preserving Data Aggregation (PPDA) serves as a fundamental component in preserving user privacy and managing demand-response (or load) in SG. However, existing PPDA schemes have limitations such as overlooking consumer billing, high computational costs, or reliance on weak security models. Furthermore, blockchain based solutions often suffer from high costs and inefficiency in data storage. To address these issues, this paper proposes a novel scheme called the computationally efficient, secure, privacy-preserving Data Aggregation for Load Monitoring and Billing Scheme (DALBS). DALBS leverages blockchain technology and Homomorphic Encryption techniques to fulfill diverse security requirements while ensuring maximum utility and accuracy for billing in SG. Through a comprehensive comparative analysis with existing state-of-the-art works, it is demonstrated that DALBS successfully meets privacy and security requirements, and ensures data utility and efficient billing, while minimizing computational and communication costs.