IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i11p2972-d179705.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/11/2972/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/11/2972/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Chang & Lin, Boqiang, 2020. "Is increasing-block electricity pricing effectively carried out in China? A case study in Shanghai and Shenzhen," Energy Policy, Elsevier, vol. 138(C).
    2. Małgorzata Sztorc, 2022. "The Implementation of the European Green Deal Strategy as a Challenge for Energy Management in the Face of the COVID-19 Pandemic," Energies, MDPI, vol. 15(7), pages 1-21, April.
    3. Leila Luttenberger Marić & Hrvoje Keko & Marko Delimar, 2022. "The Role of Local Aggregator in Delivering Energy Savings to Household Consumers," Energies, MDPI, vol. 15(8), pages 1-27, April.
    4. Salari, Mahmoud & Javid, Roxana J., 2016. "Residential energy demand in the United States: Analysis using static and dynamic approaches," Energy Policy, Elsevier, vol. 98(C), pages 637-649.
    5. Le Viet Phu, 2020. "Electricity price and residential electricity demand in Vietnam," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 22(4), pages 509-535, October.
    6. Wang, Qiang & Lin, Jian & Zhou, Kan & Fan, Jie & Kwan, Mei-Po, 2020. "Does urbanization lead to less residential energy consumption? A comparative study of 136 countries," Energy, Elsevier, vol. 202(C).
    7. Shigeru Matsumoto, 2015. "Electric Appliance Ownership and Usage: Application of Conditional Demand Analysis to Japanese Household Data," Working Papers e098, Tokyo Center for Economic Research.
    8. Park, Sungjun & Kim, Jinsoo, 2018. "The effect of interest in renewable energy on US household electricity consumption: An analysis using Google Trends data," Renewable Energy, Elsevier, vol. 127(C), pages 1004-1010.
    9. Salari, Mahmoud & Javid, Roxana J., 2017. "Modeling household energy expenditure in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 822-832.
    10. Aslam, Misbah & Ahmad, Eatzaz, 2023. "Untangling electricity demand elasticities: Insights from heterogeneous household groups in Pakistan," Energy, Elsevier, vol. 282(C).
    11. Lesley Thomson & David Jenkins, 2023. "The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets," Energies, MDPI, vol. 16(16), pages 1-29, August.
    12. Li, Zhen & Niu, Shuwen & Halleck Vega, Sol Maria & Wang, Jinnian & Wang, Dakang & Yang, Xiankun, 2024. "Electrification and residential well-being in China," Energy, Elsevier, vol. 294(C).
    13. Cartone, Alfredo & Díaz-Dapena, Alberto & Langarita, Raquel & Rubiera-Morollón, Fernando, 2021. "Where the city lights shine? Measuring the effect of sprawl on electricity consumption in Spain," Land Use Policy, Elsevier, vol. 105(C).
    14. Adom, Philip Kofi, 2017. "The long-run price sensitivity dynamics of industrial and residential electricity demand: The impact of deregulating electricity prices," Energy Economics, Elsevier, vol. 62(C), pages 43-60.
    15. Guo, Jinyu & Ma, Jinji & Li, Zhengqiang & Hong, Jin, 2022. "Building a top-down method based on machine learning for evaluating energy intensity at a fine scale," Energy, Elsevier, vol. 255(C).
    16. Guarino, Francesco & Cassarà, Pietro & Longo, Sonia & Cellura, Maurizio & Ferro, Erina, 2015. "Load match optimisation of a residential building case study: A cross-entropy based electricity storage sizing algorithm," Applied Energy, Elsevier, vol. 154(C), pages 380-391.
    17. Villareal, Maria José Charfuelan & Moreira, João Manoel Losada, 2016. "Household consumption of electricity in Brazil between 1985 and 2013," Energy Policy, Elsevier, vol. 96(C), pages 251-259.
    18. Kılkış, Şiir & Kılkış, Birol, 2019. "An urbanization algorithm for districts with minimized emissions based on urban planning and embodied energy towards net-zero exergy targets," Energy, Elsevier, vol. 179(C), pages 392-406.
    19. Yarbaşı, İkram Yusuf & Çelik, Ali Kemal, 2023. "The determinants of household electricity demand in Turkey: An implementation of the Heckman Sample Selection model," Energy, Elsevier, vol. 283(C).
    20. Grottera, Carolina & Barbier, Carine & Sanches-Pereira, Alessandro & Abreu, Mariana Weiss de & Uchôa, Christiane & Tudeschini, Luís Gustavo & Cayla, Jean-Michel & Nadaud, Franck & Pereira Jr, Amaro Ol, 2018. "Linking electricity consumption of home appliances and standard of living: A comparison between Brazilian and French households," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 877-888.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2972-:d:179705. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.