A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles

In the internet of vehicles (IoVs), vehicle users should provide location information continuously when they want to acquire continuous location-based services (LBS), which may disclose the vehicle trajectory privacy. To solve the vehicle trajectory privacy leakage problem in the continuous LBS, we propose a vehicle trajectory privacy preservation method based on caching and dummy locations, abbreviated as TPPCD, in IoVs. In the proposed method, when a vehicle user wants to acquire a continuous LBS, the dummy locations-based location privacy preservation method under road constraint is used. Moreover, the cache is deployed at the roadside unit (RSU) to reduce the information interaction between vehicle users covered by the RSU and the LBS server. Two cache update mechanisms, the active cache update mechanism based on data popularity and the passive cache update mechanism based on dummy locations, are designed to protect location privacy and improve the cache hit rate. The performance analysis and simulation results show that the proposed vehicle trajectory privacy preservation method can resist the long-term statistical attack (LSA) and location correlation attack (LCA) from inferring the vehicle trajectory at the LBS server and protect vehicle trajectory privacy effectively. In addition, the proposed cache update mechanisms achieve a high cache hit rate.

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“…The vehicle user uses the dummy location selection algorithm under road restriction (RR-DLS) [51] to generate dummy locations.…”

Section: Dummy Locations Generation Algorithmmentioning

confidence: 99%

A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles

Huang

Chen

et al. 2022

Sensors

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“…Instead of modifying the actual locations, another implementation of location perturbation is to combine the actual location with some indistinguishable dummy locations. For instance, to protect the actual locations of drivers on the internet of vehicles from being revealed by road restriction, Xu et al [16] propose a dummy-generation based location perturbation methods. The perturbed location sent to the data analyzer is composed of the actual location and several nearby fake locations following the road restriction, so it is hard for the adversary to distinguish the actual location from the dummies.…”

Section: Location Perturbationmentioning

confidence: 99%

Releasing Differentially Private Trajectories with Optimized Data Utility

Zhu

Xie

2022

Applied Sciences

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The ubiquity of GPS-enabled devices has resulted in an abundance of data about individual trajectories. Releasing trajectories enables a range of data analysis tasks, such as urban planning, but it also poses a risk in compromising individual location privacy. To tackle this issue, a number of location privacy protection algorithms are proposed. However, existing works are primarily concerned with maintaining the trajectory data geographic utility and neglect the semantic utility. Thus, many data analysis tasks relying on utility, e.g., semantic annotation, suffer from poor performance. Furthermore, the released trajectories are vulnerable to location inference attacks and de-anonymization attacks due to insufficient privacy guarantee. In this paper, to design a location privacy protection algorithm for releasing an offline trajectory dataset to potentially untrusted analyzers, we propose a utility-optimized and differentially private trajectory synthesizer (UDPT) with two novel features. First, UDPT simultaneously preserves both geographical utility and semantic utility by solving a data utility optimization problem with a genetic algorithm. Second, UDPT provides a formal and provable guarantee against privacy attacks by synthesizing obfuscated trajectories in a differentially private manner. Extensive experimental evaluations on real-world datasets demonstrate UDPT’s outperformance against state-of-the-art works in terms of data utility and privacy.

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“…Reference Characteristic k-anonymity [13,28] flexible and easy to implement; lack of location accuracy l-diversity [31,32,35] k-anonymity method considering road network Caching scheme [15,24] accurate location; not applicable for real-time service Dummy location [16,25] accurate location; false location data causing resource waste Homomorphic encryption [17,26,27] ciphertext can be calculated; high computation cost Differential privacy [18][19][20] strict proof of privacy assessment…”

Section: Privacy Preservation Mechanismmentioning

confidence: 99%

“…In order to solve this problem, many studies focused on addressing how to protect privacy in LBS during the past several years based on location obfuscation and encryption [12], such as k-anonymity [13], mixed zone [14], caching [15], dummy locations [16], homomorphic encryption [17] and differential privacy [18][19][20]. Among them, the most classic location privacy preservation scheme is k-anonymity.…”

Section: Introductionmentioning

confidence: 99%

A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack

et al. 2022

Applied Sciences

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Taking advantage of precise positioning technology, location-based service (LBS) has brought a lot of convenience to people’s daily life and made the city smarter. However, the LBS applications also bring some challenges to personal location privacy protection. In order to obtain services from LBS providers, users have to upload their queries including sensitive information, such as identities and locations. This information may be leaked out by the LBS providers or even eavesdropped on by malicious adversaries, which may cause privacy leakage. To tackle this problem, many solutions have been investigated under the assumption that users are uniformly distributed. However, the users are not always uniformly distributed in real-world situations. For a side-weight inference attack, the adversary would infer that the target user is more likely to belong to the road section with more users, resulting in performance deterioration. In this paper, we investigate the issue of location privacy preservation against side-weight inference attack for non-uniform distributed road network. Meanwhile, we consider the cost function of LBS and formulate the object as a mixed integer programming problem. Then, we propose a road truncation-based scheme to protect location privacy. The road section with high user density is designed to be truncated. Finally, simulation results show that our scheme meets the demand for privacy protection at a low cost. As a result, our scheme is proven to protect users’ location privacy effectively and efficiently.

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A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles

Cited by 19 publications

References 34 publications

A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles

A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles

Releasing Differentially Private Trajectories with Optimized Data Utility

A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack

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