Differentially Private Location Protection with Continuous Time Stamps for VANETs

Chen, Zhili; Bao, Xianyue; Ying, Zuobin; Liu, Ximeng; Zhong, Hong

doi:10.1007/978-3-030-05063-4_17

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…The vehicle generates duplicate messages in connection with the surrounding vehicle to increase vehicle identity anonymisation in the vicinity. The concept of differential privacy and pseudonym permutation is used in [46] to hide the location trajectory of the vehicle. The trajectory of the user is divided into coarse-grained and fine-grained under the personalized user privacy requirements.…”

Section: Related Workmentioning

confidence: 99%

A Distributed Mix-Context-Based Method for Location Privacy in Road Networks

et al. 2021

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Preserving location privacy is increasingly an essential concern in Vehicular Adhoc Networks (VANETs). Vehicles broadcast beacon messages in an open form that contains information including vehicle identity, speed, location, and other headings. An adversary may track the various locations visited by a vehicle using sensitive information transmitted in beacons such as vehicle identity and location. By matching the vehicle identity used in beacon messages at various locations, an adversary learns the location history of a vehicle. This compromises the privacy of the vehicle driver. In existing research work, pseudonyms are used in place of the actual vehicle identity in the beacons. Pseudonyms should be changed regularly to safeguard the location privacy of vehicles. However, applying simple change in pseudonyms does not always provide location privacy. Existing schemes based on mix zones operate efficiently in higher traffic environments but fail to provide privacy in lower vehicle traffic densities. In this paper, we take the problem of location privacy in diverse vehicle traffic densities. We propose a new Crowd-based Mix Context (CMC) privacy scheme that provides location privacy as well as identity protection in various vehicle traffic densities. The pseudonym changing process utilizes context information of road such as speed, direction and the number of neighbors in transmission range for the anonymisation of vehicles, adaptively updating pseudonyms based on the number of a vehicle neighbors in the vicinity. We conduct formal modeling and specification of the proposed scheme using High-Level Petri Nets (HPLN). Simulation results validate the effectiveness of CMC in terms of location anonymisation, the probability of vehicle traceability, computation time (cost) and effect on vehicular applications.

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Section: Related Workmentioning

confidence: 99%

A Distributed Mix-Context-Based Method for Location Privacy in Road Networks

et al. 2021

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“…Many adaptations of DP simply change the neighborhood definition to protect different types of input data than datasets. DP was adopted to graph-structured data in [38,85,136,140,144,151,153], to streaming data in [51,55,56,64,102], to symbolic control systems in [93], to text vectors in [175], to set operations in [172], to images in [174], to genomic data in [147], to recommendation systems in [80], to machine learning in [119], to location data in [28], to outsourced database systems in [101], to bandit algorithms in [12,156], to RAMs in [3,21,158] and to Private Information Retrieval in [135,155]. We detail the corresponding definitions in the full version of this work.…”

Section: Applying the Definition To Other Types Of Inputmentioning

confidence: 99%

SoK: Differential privacies

Desfontaines

Pejó

2020

Proceedings on Privacy Enhancing Technologies

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Shortly after it was first introduced in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to different scenarios and attacker models. In this work, we propose a systematic taxonomy of these variants and extensions. We list all data privacy definitions based on differential privacy, and partition them into seven categories, depending on which aspect of the original definition is modified.These categories act like dimensions: variants from the same category cannot be combined, but variants from different categories can be combined to form new definitions. We also establish a partial ordering of relative strength between these notions by summarizing existing results. Furthermore, we list which of these definitions satisfy some desirable properties, like composition, postprocessing, and convexity by either providing a novel proof or collecting existing ones.

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A Geo-indistinguishable Location Privacy Preservation Scheme for Location-Based Services in Vehicular Networks

Luo

Han

et al. 2020

Algorithms and Architectures for Parallel Processing

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Differentially Private Location Protection with Continuous Time Stamps for VANETs

Cited by 5 publications

References 22 publications

A Distributed Mix-Context-Based Method for Location Privacy in Road Networks

A Distributed Mix-Context-Based Method for Location Privacy in Road Networks

SoK: Differential privacies

A Geo-indistinguishable Location Privacy Preservation Scheme for Location-Based Services in Vehicular Networks

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