The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns. However, the use of real-world trajectory data poses significant privacy risks, such as location re-identification and correlation attacks. To address these challenges, privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data. This paper introduces DPIL-Traj, an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation. Firstly, the framework incorporates Differential Privacy Clustering, which anonymizes trajectory data by applying differential privacy techniques that add noise, ensuring the protection of sensitive user information. Secondly, Imitation Learning is used to replicate decision-making behaviors observed in real-world trajectories. By learning from expert trajectories, this component generates synthetic data that closely mimics real-world decision-making processes while optimizing the quality of the generated trajectories. Finally, Markov-based Trajectory Generation is employed to capture and maintain the inherent temporal dynamics of movement patterns. Extensive experiments conducted on the GeoLife trajectory dataset show that DPIL-Traj improves utility performance by an average of 19.85%, and in terms of privacy performance by an average of 12.51%, compared to state-of-the-art approaches. Ablation studies further reveal that DP clustering effectively safeguards privacy, imitation learning enhances utility under noise, and the Markov module strengthens temporal coherence.

The Advanced Metering Infrastructure (AMI), as a crucial subsystem in the smart grid, is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers. However, with the advancement of information and communication technology, new security and privacy challenges have emerged for AMI. To address these challenges and enhance the security and privacy of user data in the smart grid, a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance (HPPM-AMICFA) is proposed in this paper. The proposed model integrates cloud and fog computing with hierarchical threshold encryption, offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart grid. The methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance, thereby assigning appropriate protection levels. Furthermore, a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs, ensuring secure aggregation and encryption of user data. Experimental results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs, thereby safeguarding user data in the smart grid.