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

Luo, Li; Han, Zhenzhen; Xu, Chuan; Zhao, Guofeng

doi:10.1007/978-3-030-38991-8_40

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…DP methods are gaining widespread usage in safeguarding location privacy across various domains, including automotive systems. The studies in [39], [40] proposed models by deploying a GeoI-based mechanism on the Edge for LBS. However, their approach did not consider preserving vehicles' locations against the Edge.…”

Section: Related Workmentioning

confidence: 99%

“…There has been some work in this area from the perspective of just GeoI [41], [40], [39], [60], [61] or just from the standpoint of generating dummy locations exploiting anonymisation techniques [62], [63]. One of the first major concerns in using only GeoI is when we allow dynamic and multiple queries along the journey of the EVs, as individual locations, despite being privatised, can still be interpolated to approximate the entire trace.…”

Section: ) Impact Of Dummy Data Generationmentioning

confidence: 99%

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A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Atmaca,

Biswas,

Maple

et al. 2024

IEEE Open J. Veh. Technol.

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Electric vehicles (EVs) are becoming more popular due to environmental consciousness. However, the limited availability of charging stations (CSs) compared to the number of EVs on the road has increased range anxiety and CS queries during trips. Simultaneously, personal data use for various forms of analytics is growing at an unprecedented rate, raising concerns about privacy violations. One standard for formalising location privacy is geo-indistinguishability as a generalisation of local differential privacy. However, the noise has to be carefully calibrated considering the implications of potential utility loss. In this paper, we introduce the notion of approximate geo-indistinguishability (AGeoI) which allows the EVs to obfuscate the individual query locations while ensuring that they remain within their preferred area of interest. It is vital because journeys are often sensitive to a sharp drop in quality of service (QoS), which has a high cost for the extra distance to be covered. Our proposed method combines the application of AGeoI and the generation of dummy data to provide two-fold privacy protection (individual query locations and the trajectory of the entire journeys) for EVs while preserving a high level of QoS. Analytical insights and experiments are used to demonstrate that a very high percentage of EVs get "privacy for free" and that the utility loss caused by the gain in privacy guarantees is minuscule. Aside from harbouring a high QoS for the EVs, using the iterative Bayesian update, our method allows for a private and precise prediction occupancies of CSs which is vital in unprecedented traffic congestion scenarios and efficient route-planning.

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Section: Related Workmentioning

confidence: 99%

Section: ) Impact Of Dummy Data Generationmentioning

confidence: 99%

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Atmaca,

Biswas,

Maple

et al. 2024

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…Planar Laplace distribution [36]: Given ε ∈ R 2 , for the actual position x, for any approximate position z ∈ R 2 generated by the mechanism, the probability density function is…”

Section: Geo-indistinguishabilitymentioning

confidence: 99%

PPVC: Towards a Personalized Local Differential Privacy-Preserving Scheme for V2G Charging Networks

Qin,

Wang

2023

Mathematics

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The rapid development of electric vehicles provides users with convenience of life. When users enjoy the V2G charging service, privacy leakage of their charging location is a crucial security issue. Existing privacy-preserving algorithms for EV access to charging locations suffer from the problem of nondefendable background knowledge attacks and privacy attacks by untrustworthy third parties. We propose a personalized location privacy protection scheme (PPVC) based on differential privacy to meet users’ personalized EV charging requirements while protecting their privacy. First, by constructing a decision matrix, PPVC describes recommended routes’ utility and privacy effects. Then, a utility model is constructed based on the multiattribute theory. The user’s privacy preferences are integrated into the model to provide the route with the best utility. Finally, considering the privacy preference needs of users, the Euclidean distance share is used to assign appropriate privacy budgets to users and determine the generation range of false locations to generate the service request location with the highest utility. The experimental results show that the proposed personalized location privacy protection scheme can meet the service demands of users while reasonably protecting their privacy to provide higher service quality. Compared with existing solutions, PPVC improves the charging efficiency by up to 25%, and 8% at the same privacy protection level.

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“…DP methods are increasingly being deployed to preserve location privacy in a variety of domains, including automotive systems. The studies in [38,56] proposed models by deploying a GeoI-based mechanism on the Edge for location-based services. However, their approach did not consider preserving vehicles' location privacy against the Edge.…”

Section: Related Workmentioning

confidence: 99%

A privacy preserving querying mechanism with high utility for electric vehicles

Atmaca¹,

Biswas²,

Maple³

et al. 2022

Preprint

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Recently, with the rising awareness about advancing towards a sustainable future, electric vehicles (EVs) are becoming more and more popular. As there are disproportionately fewer charging stations than EVs, range anxiety plays a major role in the rise in the number of queries made by EVs along their journeys to find or schedule a slot in the nearest available charging station, and a significant portion of such online querying is handled by third-party service providers and communicated via the Edge cloud. On the other hand, with the recent drive towards an information-based society, the use of personal data in various kinds of analytics is surging like never before. Hence, privacy threats to personal data and the risk of privacy violation are also increasing manifold. One of the recently popularised methods formalising utility-preserving location privacy is geo-indistinguishability (GeoI), a generalisation of the local variant of differential privacy (DP), which the state-of-the-art standard for privacy protection. However, the noise should be calibrated carefully, taking into account the implications for potential utility-loss for the users affecting their quality of service (QoS). In this paper, we focus on the environment where EVs dynamically query about charging stations along their journeys, and we introduce the idea of approximate geo-indistinguishability (AGeoI) on road networks which allows the EVs to obfuscate the individual query locations while ensuring that they remain within their preferred area of interest, as journeys are very sensitive to a sharp loss in QoS which may incur a high cost for the extra distance to be covered due to privacy. We propose a novel method to protect the privacy of EVs, both for the individual query locations and against the threat of tracing the trajectory of their journeys from adversarial third-parties, by applying AGeoI that enables the users to substantially preserve their QoS. We lay out an analytical insight on the working of our method and experimentally illustrate that under our method a very high fraction of the EVs attain privacy for free, and, in general, the utility-loss suffered due to gain in privacy is significantly low.

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

Cited by 4 publications

References 14 publications

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

PPVC: Towards a Personalized Local Differential Privacy-Preserving Scheme for V2G Charging Networks

A privacy preserving querying mechanism with high utility for electric vehicles

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