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The Location Privacy Protection of Electric Vehicles with Differential Privacy in V2G Networks

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  • Yuancheng Li

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Pan Zhang

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
    State Grid Information & Telecommunication Branch, Beijing 100761, China)

  • Yimeng Wang

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

Vehicle-to-grid (V2G) is an important component of smart grids and plays a significant role in improving grid stability, reducing energy consumption and generating cost. However, while electric vehicles are being charged, it is possible to expose the location and movement trajectories of the electric vehicles, thereby triggering a series of privacy and security issues. In response to this problem, we propose a new quadtree-based spatial decomposition algorithm to protect the location privacy of electric vehicles. First of all, we use a random sampling algorithm, which is based on differential privacy, to obtain enough spatial data to achieve the balance between large-scale spatial data and the amount of noise. Secondly, in order to overcome the shortcomings of using tree height to control Laplacian noise in the quadtree, we use sparse vector technology to control the noise added to the tree nodes. Finally, according to the vehicle-to-grid network structure in the smart grid, we propose a location privacy protection model based on distributed differential privacy technology for EVs in vehicle-to-grid networks. We demonstrate application of the proposed model in real spatial data and show that it can achieve the best effect on the security of the algorithm and the availability of data.

Suggested Citation

  • Yuancheng Li & Pan Zhang & Yimeng Wang, 2018. "The Location Privacy Protection of Electric Vehicles with Differential Privacy in V2G Networks," Energies, MDPI, vol. 11(10), pages 1-17, October.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2625-:d:173259
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    References listed on IDEAS

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    4. Michael Child & Alexander Nordling & Christian Breyer, 2018. "The Impacts of High V2G Participation in a 100% Renewable Åland Energy System," Energies, MDPI, vol. 11(9), pages 1-19, August.
    5. Geske, Joachim & Schumann, Diana, 2018. "Willing to participate in vehicle-to-grid (V2G)? Why not!," Energy Policy, Elsevier, vol. 120(C), pages 392-401.
    6. Tohid Harighi & Ramazan Bayindir & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Eklas Hossain, 2018. "An Overview of Energy Scenarios, Storage Systems and the Infrastructure for Vehicle-to-Grid Technology," Energies, MDPI, vol. 11(8), pages 1-18, August.
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    Cited by:

    1. Jesús Rodríguez-Molina & Pedro Castillejo & Victoria Beltran & Margarita Martínez-Núñez, 2020. "A Model for Cost–Benefit Analysis of Privately Owned Vehicle-to-Grid Solutions," Energies, MDPI, vol. 13(21), pages 1-38, November.
    2. Nnaemeka Vincent Emodi & Scott Dwyer & Kriti Nagrath & John Alabi, 2022. "Electromobility in Australia: Tariff Design Structure and Consumer Preferences for Mobile Distributed Energy Storage," Sustainability, MDPI, vol. 14(11), pages 1-18, May.

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