Location Privacy Protection in Vehicular Networks
- Publication Type:
- Thesis
- Issue Date:
- 2023
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Location privacy is of utmost importance in vehicular networks, where drivers’ trajectories and personal information can be exposed, posing threats to drivers’ safety and personal security. The proliferation of Location-Based Services (LBS) has led to a rapid increase in location
data, thereby amplifying the risk to location privacy. In road networks, vehicles share location data with other vehicles and LBS through the Internet of Vehicles (IoV), making the need for effective location obfuscation techniques crucial.
Existing obfuscation mechanisms primarily focus on Two-Dimensional (2D) planar areas and overlook the unique features of road networks, often resulting in impractical outcomes such as off-road locations. Fake trajectories created by adversaries and malicious drivers can significantly compromise the utility of location data in IoV and degrade the quality of LBS. Therefore, it is essential to detect illegal trajectories to ensure the utility of location data in IoV. Some existing methods try to overcome these limitations by using pseudonyms and obfuscation, but the additive nature of differential privacy has been overlooked.
In this thesis, we propose a comprehensive differential privacy framework for protecting location privacy in vehicular networks by considering the correlation between location data and driving statuses. We first propose a personalized obfuscation mechanism that dynamically and adaptively protects the location privacy of drivers in road networks. We also define a new notion of Road Network-Indistinguishability (RN-I) to evaluate obfuscation-based mechanisms in road networks and propose a Personalized Location Privacy-Preserving (PLPP) mechanism that achieves RN-I for a single vehicle. Using the proposed RN-I, we then leverage differential privacy and propose a Cloaking Region Obfuscation (CRO) mechanism that safeguards the location privacy of multiple vehicles in road networks. To address the limitation that differential privacy makes the detection of illegal trajectories challenging, we propose a comprehensive framework for protecting location privacy in IoV by detecting illegal trajectories while preserving data utility. Finally, we introduce a new notion of Trajectory-Indistinguishability (T-I) by combining pseudonym swapping and RN-I to measure the indistinguishability of vehicles in road networks and design a Joint Trajectory Obfuscation and Pseudonym Swapping (JTOPS) mechanism that achieves T-I.
Experiments upon real-world datasets confirm the location and identity privacy-preserving capability, data utility, and efficiency of the proposed mechanisms.
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