A Decentralized Location Privacy-Preserving Spatial Crowdsourcing for Internet of Vehicles

Zhang, Junwei; Yang, Fan; Ma, Zhuo; Wang, Zhuzhu; Liu, Ximeng; Ma, Jianfeng

doi:10.1109/tits.2020.3010288

Cited by 61 publications

(14 citation statements)

References 36 publications

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“…In [11] the authors investigate vehicle-based spatial crowdsourcing (SC) applications whereby workers have to upload their driving locations. They propose a decentralized, privacypreserving solution for such Internet of Vehicles SC using homomorphic encryption, zeroknowledge proof and circle-based location verification.…”

Section: Literature Review Resultsmentioning

confidence: 99%

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Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Rannenberg¹,

Pape²,

Tronnier³

et al. 2021

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The aim of this study was to identify and evaluate different de-identification techniques that may be used in several mobility-related use cases. To do so, four use cases have been defined in accordance with a project partner that focused on the legal aspects of this project, as well as with the VDA/FAT working group. Each use case aims to create different legal and technical issues with regards to the data and information that are to be gathered, used and transferred in the specific scenario. Use cases should therefore differ in the type and frequency of data that is gathered as well as the level of privacy and the speed of computation that is needed for the data. Upon identifying use cases, a systematic literature review has been performed to identify suitable de-identification techniques to provide data privacy. Additionally, external databases have been considered as data that is expected to be anonymous might be reidentified through the combination of existing data with such external data. For each case, requirements and possible attack scenarios were created to illustrate where exactly privacy-related issues could occur and how exactly such issues could impact data subjects, data processors or data controllers. Suitable de-identification techniques should be able to withstand these attack scenarios. Based on a series of additional criteria, de-identification techniques are then analyzed for each use case. Possible solutions are then discussed individually in chapters 6.1 - 6.2. It is evident that no one-size-fits-all approach to protect privacy in the mobility domain exists. While all techniques that are analyzed in detail in this report, e.g., homomorphic encryption, differential privacy, secure multiparty computation and federated learning, are able to successfully protect user privacy in certain instances, their overall effectiveness differs depending on the specifics of each use case.

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Section: Literature Review Resultsmentioning

confidence: 99%

“…Figure 8. System model using blockchain technology by [11] The solution results in a grid where workers are located in, whereby the size of the grid demonstrates the achieved level of privacy. The authors state that their solution is efficient and feasible in real-life use cases.…”

Section: Literature Review Resultsmentioning

confidence: 99%

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Rannenberg¹,

Pape²,

Tronnier³

et al. 2021

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show abstract

“…Using a trustedthird-party (TTP) to manage the location information is not a very pragmatic approach to cater different privacy demand of the users. In [122], the authors introduce a blockchain based decentralised location privacy-preserving SC for IoVs. They exploit circle-based location verification and homomorphic encryption to assure the confidentiality of the location policy with multi-level privacy preservation for workers' location.…”

Section: Privacy Issues In Iov Communicationsmentioning

confidence: 99%

Towards 6G-Enabled Internet of Vehicles: Security and Privacy

Osorio

Ahmad

Gurtov

et al. 2022

IEEE Open J. Commun. Soc.

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The conceptualisation of the sixth generation of mobile wireless networks (6G) has already started with some potential disruptive technologies resonating as enablers for driving the emergence of a number of innovative applications. Particularly, 6G will be a prominent supporter for the evolution towards a truly Intelligent Transportation System and the realisation of the Smart City concept by fulfilling the limitations of 5G, once vehicular networks are becoming highly dynamic and complex with stringent requirements on ultra-low latency, high reliability, and massive connections. More importantly, providing security and privacy to such critical systems should be a top priority as vulnerabilities can be catastrophic, thus there are huge concerns regarding data collected from sensors, people and their habits. In this paper, we provide a timely deliberation of the role that promissory 6G enabling technologies such as artificial intelligence, network softwarisation, network slicing, blockchain, edge computing, intelligent reflecting surfaces, backscatter communications, terahertz links, visible light communications, physical layer authentication, and cell-free massive multiple-input multiple-output (MIMO) will play on providing the expected level of security and privacy for the Internet of Vehicles.

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“…e decentralized location privacypreserving method was proposed by Zhang et al [16] to protect the location privacy of task and achieve the multilevel location privacy preserving of workers. By making use of the decentralized structure and consensus approach of Blockchain, Zou et al [17] proposed the two-stage approach realize the nonrepudiation and nontampering of data.…”

Section: Blockchain-based Location Privacymentioning

confidence: 99%

Blockchain-Enabled Privacy-Preserving Location Sharing Scheme for LBSNs

Zhu

Liu

et al. 2021

Mobile Information Systems

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The rise of Internet of Things (IoT) technology promotes the rapid development of location services industry. The idea of smart connectivity also provides a new direction for Location-Based Social Networks (LBSNs). However, due to limited calculate ability and internal storage space of IoT devices, historical location data of users is generally stored in the central server, which is likely to cause the disclosure of users’ private data. In this paper, we propose a Blockchain-enabled Privacy-Preserving Location Sharing (B-PPLS) scheme, which is a new framework that not only protects user location privacy but also provides effective location sharing services for users. For B-PPLS, location data owners can share the location area instead of location coordinates to Requesters, in order to realize the location privacy preserving. Also, the Merkle hash tree is utilized to divide the location area, so as to realize the multilevel privacy preserving. Furthermore, four algorithms are proposed to achieve the four stages of initialization, location record, location sharing, and location verification, respectively. Finally, we analyze the security of the proposed B-PPLS scheme and compare the performance with other related location privacy-preserving schemes by experimental evaluation.

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A Decentralized Location Privacy-Preserving Spatial Crowdsourcing for Internet of Vehicles

Cited by 61 publications

References 36 publications

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Towards 6G-Enabled Internet of Vehicles: Security and Privacy

Blockchain-Enabled Privacy-Preserving Location Sharing Scheme for LBSNs

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