Privacy-preserving solution for vehicle parking services complying with EU legislation

被引:0
作者
Dzurenda P. [1 ]
Jacques F. [2 ]
Knockaert M. [2 ]
Laurent M. [3 ]
Malina L. [1 ]
Matulevicius R. [4 ]
Tang Q. [5 ]
Tasidou A. [3 ]
机构
[1] Department of Telecommunications, Brno University of Technology, Brno
[2] University of Namur, Namur
[3] Samovar, Télécom SudParis, Institut Polytechnique de Paris, Paris
[4] Institute of Computer Science, University of Tartu, Tartu
[5] IT for Innovative Services Department, Luxembourg Institute of Science and Technology
基金
欧盟地平线“2020”;
关键词
Intelligent infrastructure; Legislation; Parking services; Privacy by design; Privacy preserving statistical analysis; Privacy preserving technology;
D O I
10.7717/PEERJ-CS.1165
中图分类号
学科分类号
摘要
Today, many modern cities adopt online smart parking services as best practices. Citizens can easily access these services using their smartphones or the infotainment panels in their cars. These services' primary objective is to give drivers the ability to quickly identify free parking slots, which should reduce parking time, save fuel, and relieve traffic in urban areas. However, the privacy offered by these services should be comparable to that of the standard paper-based parking solutions offered by parking ticket machines. On the other hand, a privacy-preserving smart parking service's design may raise a number of issues, including how to prevent double or multiple uses of parking tickets, how to prevent user tracking and profiling, how to revoke malicious users, how to handle data statistics without violating users' privacy, and how to comply with regulations like the General Data Protection Regulation (GDPR). In this article, we present multidisciplinary research on a comprehensive vehicle parking system that protects users' privacy. The research includes a range of topics, from the examination of regulatory compliance to the design of privacy-preserving parking registration and vehicle parking services to the implementation of privacy-preserving parking data processing features for data analysts. We provide a security analysis of our concept as well as several experimental results. © 2022 Dzurenda et al.
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