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Local Differential Privacy-Based Privacy-Preserving Data Range Query Scheme for Electric Vehicle Charging | IEEE Journals & Magazine | IEEE Xplore

Local Differential Privacy-Based Privacy-Preserving Data Range Query Scheme for Electric Vehicle Charging

Publisher: IEEE

Abstract:

The local differential privacy (LDP)-based data range query method has gained significant attention in the field of electric vehicles (EVs). Due to the reliance of most E...View more

Abstract:

The local differential privacy (LDP)-based data range query method has gained significant attention in the field of electric vehicles (EVs). Due to the reliance of most EV charging services on third-party platforms, owner privacy is easily compromised. The range query is critical in addressing this problem. However, the current strategies face critical challenges in balancing privacy, accuracy, and efficiency. In this paper, we propose a privacy-preserving data range query (PPQ) scheme based on LDP. This innovative scheme comprises a data encryption optimization model, a data storage optimization (DSO) model, and a secure hierarchical decomposition (SHD) algorithm. The optimized unary encoding (OUE) protocol is used to solve privacy and accuracy issues. Furthermore, the efficiency problem is solved by optimizing the data storage structure and improving the speed of data aggregation using the DSO model and the SHD algorithm. The effectiveness of the PPQ scheme is verified by simulating V2G scenarios in different datasets. The discussion illustrates that the PPQ scheme can withstand various attacks, including collusion attacks, background knowledge attacks, membership inference attacks, and distributed denial of service attacks. It can also ensure query accuracy and query efficiency while ensuring data privacy.
Published in: IEEE Transactions on Network Science and Engineering ( Volume: 11, Issue: 1, Jan.-Feb. 2024)
Page(s): 673 - 684
Date of Publication: 15 August 2023

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Publisher: IEEE

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