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.

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.

SUMMARY: Construction of high-quality fragment libraries by segmenting organic compounds is an important part of the drug discovery paradigm. This article presents a new method, MacFrag, for efficient molecule fragmentation. MacFrag utilized a modified version of BRICS rules to break chemical bonds and introduced an efficient subgraphs extraction algorithm for rapid enumeration of the fragment space. The evaluation results with ChEMBL dataset exhibited that MacFrag was overall faster than BRICS implemented in RDKit and modified molBLOCKS. Meanwhile, the fragments acquired through MacFrag were more compliant with the 'Rule of Three'. AVAILABILITY AND IMPLEMENTATION: https://github.com/yydiao1025/MacFrag. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.

Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of 'near-drug' properties is still a challenging task. In this work, we proposed a multimodal chemical information reconstruction system to automatically process, extract and align heterogeneous information from the text descriptions and structural images of chemical patents. Our key innovation lies in a heterogeneous data generator that produces cross-modality training data in the form of text descriptions and Markush structure images, from which a two-branch model with image- and text-processing units can then learn to both recognize heterogeneous chemical entities and simultaneously capture their correspondence. In particular, we have collected chemical structures from ChEMBL database and chemical patents from the European Patent Office and the US Patent and Trademark Office using keywords 'A61P, compound, structure' in the years from 2010 to 2020, and generated heterogeneous chemical information datasets with 210K structural images and 7818 annotated text snippets. Based on the reconstructed results and substituent replacement rules, structural libraries of a huge number of near-drug compounds can be generated automatically. In quantitative evaluations, our model can correctly reconstruct 97% of the molecular images into structured format and achieve an F1-score around 97-98% in the recognition of chemical entities, which demonstrated the effectiveness of our model in automatic information extraction from chemical patents, and hopefully transforming them to a user-friendly, structured molecular database enriching the near-drug space to realize the intelligent retrieval technology of chemical knowledge.

A common misconception concerning Insider Threat is that the information infrastructure is at considerable risk from technical issues. In fact, Insider Threat is a multidisciplinary concept across many different fields, including personnel security, environment security and technology security. All aspects regarding Insider Threat must be addressed in a well-structured and holistic manner, failure of which may result in security breaches. The aim of this paper is to provide an integrated and holistic approach to establish a security and defense architecture for Insider Threat.