ABSTRACT With the rapid development of the Internet of Vehicles (IoV) and location‐based services (LBS), the privacy and security of trajectory data have become a top priority. Disclosure of trajectory privacy may pose many risks to users. To solve this problem, this paper proposes a vehicle trajectory data protection scheme combining regional realizability and deep learning (RDPP). Firstly, a regional realizability processing is proposed, which divides and covers geographical areas according to road network density and then defines the trajectory generation restrictions. Secondly, this paper proposed a combined regional realizability of the trajectory data generation model (RRP‐TrajGAN) that can combine the trajectory generation restrictions to generate trajectory data that is in line with the real situation. Finally, the proposed personalized privacy budget allocation method based on the clustering and density method (CD‐DP) is used to cluster the generated trajectory data, and a reasonable privacy budget is allocated to the trajectory data according to the clustering density attribute. Compared with more advanced schemes, this paper's approach uniquely combines regional realizability processing with deep generative models and density‐based privacy budget allocation, achieving a balance between privacy and utility without sacrificing real‐world feasibility. The experimental results show that compared with other existing schemes, the proposed scheme's degree of privacy protection is improved by 11.88%–39.82%, while data availability can be well guaranteed. In addition, the time complexity of the proposed scheme is , which is better than the comparison scheme.

Spatial crowdsourcing (SC) recruits online workers to perform geographically relevant tasks based on their real time locations, which exposes sensitive location information. Workers’ heterogeneous privacy needs make uniform protection ineffective and degrade service quality. To address these issues, this paper proposes a deep reinforcement learning-based personalized privacy protection scheme for spatial crowdsourcing. First, the proposed scheme constructs a personalized privacy protection model based on spatiotemporal features, which dynamically assigns personalized privacy protection intensity to different users by quantitatively analyzing the association between factors such as location, semantic sensitivity, access frequency, and access duration with individual privacy needs. Secondly, deep reinforcement learning algorithms are employed to adjust the model dynamically according to changes in the environment, thereby achieving an adaptive balance between privacy protection and task completion efficiency, and a bidirectional privacy-preserving mechanism combining Local Differential Privacy (LDP) and homomorphic encryption is designed to protect the privacy of both workers and requesters. Again, to achieve a more comprehensive task allocation strategy, this scheme proposes a multi-factor optimized task assignment mechanism that combines parameters such as worker reputation and task response latency. Finally, compared with the existing schemes, the proposed scheme can maintain a high accuracy rate for task assignment while ensuring personalized privacy protection, thereby improving the efficiency and reliability of the crowdsourcing system.

ABSTRACT In distributed contexts, pedestrian trajectory prediction faces data silos, making cross‐scene data sharing difficult. Centralised training and synchronised federated learning pose risks of privacy breaches, as attackers may extract sensitive information through model inversion techniques. This paper presents a privacy‐secure asynchronous federated multimodal pedestrian trajectory prediction model (AFed‐MTP) to enhance global update efficiency and reduce reliance on delayed nodes through dynamic aggregation, as traditional synchronous training diminishes efficiency due to discrepancies in node performance, resulting in postponed global updates that affect real‐time applications. This scheme introduces a multimodal trajectory prediction model based on generative adversarial networks (GAN‐MTP) for each scenario, integrating spatiotemporal graph networks with a generative adversarial framework to generate multimodal trajectories during localised training, thereby reducing data leakage and ensuring strong privacy protection. Experimental results show that this scheme outperforms the method trained directly across various scenarios regarding data privacy security, with the mutual information value reduced to 0.018 by replacing real data with locally predicted trajectories, thereby improving privacy protection efficacy by 25%. In decentralised contexts, the ADE prediction errors for the FD1 and FD2 datasets decrease significantly compared to previous methodologies by 31.7% and 31.9%, respectively. This framework strikes a balance between privacy preservation and predictive accuracy, offering practical and safe solutions for applications such as autonomous driving and smart cities.

A trajectory privacy protection method using cached candidate result sets (TPP-CCRS) is proposed for the user trajectory privacy leakage problem. First, the user's area is divided into a grid to lock the user's trajectory range, and a cache area is set on the user's mobile side to cache the candidate result sets queried from the user's area. Second, a security center is deployed to register users securely and assign public and private keys for verifying location information . The same user's location information is randomly divided into M copies and sent to multi-anonymizers. Then, the random concurrent k -anonymization mechanism with multi-anonymizers is used to concurrently k -anonymize M copies of location information. Finally, the prefix tree is added on the location-based service (LBS) server side, and the location information is encrypted using the clustered data fusion privacy protection algorithm. The optimal binary tree algorithm queries user interest points. Security analysis and experimental verification show that the TPP-CCRS can effectively protect user trajectory privacy and improve location information query efficiency.

The proliferation of location-based service applications has led to a substantial surge in the amount of life trajectory data produced by mobile devices. And these data frequently contain confidential personal details. Simultaneously, the corresponding relatively lagging privacy protection technology and the improper trajectory data handling method will make tremendous problems with privacy breach. Therefore, this paper presents a trajectory privacy data publishing scheme, denoted as LORDP, which is based on local optimisation and R-tree. The proposed scheme aims to handle sensitive data while improves trajectory protection effectiveness. Firstly, the scheme combines the LKC-privacy model requirement to filter out the minimum violating sequences set, to reduce data sensitivity and the amount of injected noise. Secondly, R-tree is constructed based on trajectory similarity. Finally, Laplacian noise is added to the R-tree’s leaf nodes constrained by differential privacy. The experiments show that the proposed LORDP algorithm significantly enhances the utility of data compared to other algorithms, and reduces the loss rate of about approximately 2% for per trajectory data, which shows that the present algorithm is extremely effective.

Japan's Health Care System has experienced a number of changes over the last half a century. And the most heated aspect of current health policy debate focuses on the potential radical reform of the medical care system. In addition, the regional medical service organizations are in front of a more severe challenge because of the rapidly aging population and decreasing young population. Consequently, hospitals and other NHS organizations in local areas have been required to introduce reforms in their management structure for their survival This paper reports in depth on a case study of a private Japanese hospital, the Chikamori medical group, to introduce a key competence-based management model and differentiation marketing strategy. As one of the regional medical care support hospitals, Chikamori plays an active and important role in the regional medical care network. The case implies the effective business model based on positioning in the network architecture, which is significant not only for medical service field but also for other regional industries. The mutual correlations among financial strategy, human resource design, managing policy and collaboration manner with regional doctors are discussed in the paper.