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A Privacy-Preserving Noise Addition Data Aggregation Scheme for Smart Grid

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  • Yuwen Chen

    (Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIST), Universidad Politécnica de Madrid (UPM), C/Nikola Tesla, s/n, 28031 Madrid, Spain)

  • José-Fernán Martínez

    (Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIST), Universidad Politécnica de Madrid (UPM), C/Nikola Tesla, s/n, 28031 Madrid, Spain)

  • Pedro Castillejo

    (Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIST), Universidad Politécnica de Madrid (UPM), C/Nikola Tesla, s/n, 28031 Madrid, Spain)

  • Lourdes López

    (Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIST), Universidad Politécnica de Madrid (UPM), C/Nikola Tesla, s/n, 28031 Madrid, Spain)

Abstract

Smart meters are applied to the smart grid to report instant electricity consumption to servers periodically; these data enable a fine-grained energy supply. However, these regularly reported data may cause some privacy problems. For example, they can reveal whether the house owner is at home, if the television is working, etc. As privacy is becoming a big issue, people are reluctant to disclose this kind of personal information. In this study, we analyzed past studies and found that the traditional method suffers from a meter failure problem and a meter replacement problem, thus we propose a smart meter aggregation scheme based on a noise addition method and the homomorphic encryption algorithm, which can avoid the aforementioned problems. After simulation, the experimental results show that the computation cost on both the aggregator and smart meter side is reduced. A formal security analysis shows that the proposed scheme has semantic security.

Suggested Citation

  • Yuwen Chen & José-Fernán Martínez & Pedro Castillejo & Lourdes López, 2018. "A Privacy-Preserving Noise Addition Data Aggregation Scheme for Smart Grid," Energies, MDPI, vol. 11(11), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2972-:d:179705
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    References listed on IDEAS

    as
    1. Wiesmann, Daniel & Lima Azevedo, Inês & Ferrão, Paulo & Fernández, John E., 2011. "Residential electricity consumption in Portugal: Findings from top-down and bottom-up models," Energy Policy, Elsevier, vol. 39(5), pages 2772-2779, May.
    2. Yuwen Chen & José-Fernán Martínez & Pedro Castillejo & Lourdes López, 2017. "An Anonymous Authentication and Key Establish Scheme for Smart Grid: FAuth," Energies, MDPI, vol. 10(9), pages 1-23, September.
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    Cited by:

    1. Marta Moure-Garrido & Celeste Campo & Carlos Garcia-Rubio, 2022. "Entropy-Based Anomaly Detection in Household Electricity Consumption," Energies, MDPI, vol. 15(5), pages 1-21, March.

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