A Personalized Secondary Perturbation Mechanism Based on Local Differential Privacy

With the development of location-based services (LBS) in mobile internet and smart devices, privacy leakage concerns are escalating. While differential privacy has gained traction for location protection due to its rigorous guarantees, existing methods face two critical challenges: repeatedly applying identical mechanisms at a single location enables attackers to leverage background knowledge for inference attacks, compromising true location privacy; meanwhile, they lack personalized privacy configurations and associated service quality metrics. To address these issues, we propose PSPLDP, implementing dual Laplace perturbation under local differential privacy to fulfill location specific privacy requirements. And we introduce a service quality evaluation metric suitable for personalized privacy settings. Experiments on real world datasets demonstrate our method's effectiveness against inference attacks and the capability of the metric to quantify service quality across diverse privacy settings.