A Threat-Aware Differential Privacy Protection Method with Dual-Factor Dynamic Adjustment

To address the dual challenges of static privacy budgets and declining data utility in trajectory sharing, this paper proposes a threat-aware differential privacy protection method with dual-factor dynamic adjustment. A lightweight LSTM-Lite model is employed to perceive and quantify environmental threats in real time, thereby dynamically linking threat scores with privacy budget adjustment and reducing the disconnection between threat detection and parameter control. Based on threat scores and semantic sensitivity, a dual-factor controller is designed to enable adaptive regulation of the privacy budget ϵ, thereby improving the adaptability of privacy protection in adversarial environments. Furthermore, a Bayesian-inference-based trajectory consistency reconstruction algorithm is developed to mitigate trajectory jitter and drift caused by Laplace noise. The proposed algorithm calibrates the filtering gain through posterior estimation and incorporates kinematic constraints to improve reconstruction quality. Experimental results show that the proposed method can accurately identify abnormal threats, achieving an F1-score of 0.885, and adaptively regulate privacy intensity under dynamically changing risks. Compared with fixed-budget and single-stage recovery methods, the proposed approach achieves a better balance between privacy protection and trajectory utility, effectively reducing spatial deviation and speed jitter in perturbed trajectories.