Personalized Continuous Location Privacy Protection in Internet of Vehicles

In Internet of Vehicles (IoV), location-based services (LBS) pose risks of location privacy breaches. Existing differential privacy methods employ a uniform privacy budget allocation strategy in continuous request scenarios, which struggles to accommodate personalized protection needs and often leads to an imbalance between privacy protection strength and service quality. To address this issue, this paper proposes a personalized location privacy protection method tailored for continuous requests. By employing fuzzy mathematical theory to hierarchically quantify semantic location sensitivity, the method resolves discrepancies in users' perceptions of sensitive areas. It constructs a location privacy level assessment model by integrating users' subjective preference characteristics with their mobile behavior patterns. Subsequently, personalized privacy budgets are dynamically allocated based on location privacy levels and proximity to sensitive points. Finally, the paper introduces a noise mechanism based on a planar truncated Laplace distribution, utilizing this mechanism to perturb each location within continuous requests. Security proofs demonstrate that the method satisfies approximate differential privacy. Experimental evaluations confirm that this approach not only enhances service quality but also showcases the advantages of personalized and dynamically adaptive privacy protection.