With the rapid development of high-speed mobile network technology and high-precision positioning technology, the trajectory information of mobile users has received extensive attention from academia and industry in the field of Location-based Social Networks. Researchers can mine users’ trajectories in Location-based Social Networks to obtain sensitive information, such as friendship groups, activity patterns, and consumption habits. Therefore, mobile users’ privacy and security issues have received growing attention in Location-based Social networks. It is crucial to strike a balance between privacy protection and data availability. This paper proposes a differential privacy trajectory protection method based on latent interest community detection (DPTP-LICD), ensuring strict privacy protection standards and user data availability. Firstly, based on the historical trajectory information of users, spatiotemporal constraint information is extracted to construct a potential community strength model for mobile users. Secondly, the latent interest community obtained from the analysis is used to identify preferred hot spots on the user’s trajectory, and their priorities are assigned based on a popularity model. A reasonable privacy budget is allocated to prevent excessive noise from being added and rendering the protected trajectory data unusable. Finally, to prevent privacy leakage, we add Laplace and exponential noise in generating preferred hot spots and recommending user interest points. Security and effectiveness analysis shows that our mechanism provides effective points of interest recommendations and protects users’ privacy from disclosure.

At present, with the popularization of intelligent equipment. Almost every smart device has a GPS. Users can use it to obtain convenient services, and third parties can use the data to provide recommendations for users and promote relevant business development. However, due to the large number of location data, there are serious data sparsity problems in the data uploaded by users. At the same time, with great value comes great danger. Once the user’s location information is obtained by the attacker, severe security issues will be caused. In recent years, a lot of researchers have studied the recommendation of point of interests (POIs) and the privacy protection of location. Yet, few of them have explored both together, which induces some drawbacks on the combination of them. This paper combines POI recommendation with a privacy protection mechanism. Besides providing user with POI recommendation service, it also protects the privacy of user’s location. We proposed a POI recommendation model with privacy protection mechanism, termed POI recommendation model for community groups based on privacy protection (CGPP‐POI). This model can ensure the recommendation accuracy and reduce the leakage of user location information via taking advantages of the characteristics of location. At the same time, it deals with the problem of poor recommendation performance caused by sparse data. In addition, through the expansion of location, random and other methods are used to protect the user’s real check‐in information. First, the data processed at the terminal satisfied local differential privacy. At the same time, we use the data to build a recommendation model. Then, we use a community of user in the model to improve the availability of these disturbed data, explore the relationship between users, and expand check‐ins within the community. Finally, we provide the POI recommendations to users. Based on the traditional evaluation criteria, we adopted four metrics, i.e., accuracy, recall rate, coverage rate, and popularity in evaluation part, where intensive experiments conducted on real datasets Gowalla and Brightkite demonstrate that our approach outperforms the baseline methods significantly.

The development of 5G technology has driven the rise of e‐commerce, social networking, and the Internet of Things. Under the high‐speed transmission, the data volume increases, and the user demand also changes. Personalized customization has become the mainstream trend of network development. However, as the speed of the Internet increases, a series of problems also arise. The increase in data volume results in a reduction of bandwidth, a growth of the central processor’s pressure, and a higher risk of data leakage. A search system and a recommendation platform are the tools to improve people’s search efficiency. However, providing personalized recommendations to different users according to their needs is still an urgent problem. Simultaneously, the big data volume means that attackers can also get more information. They can use background knowledge and various reasoning methods to deduce the user’s private information using nonprivate items. In this paper, the solutions to safe and reliable recommendation services are the main problem explored. Based on this idea, this paper proposed short‐term dynamic recommendation model based on local differential privacy (SDRM‐LDP). This model uses a small amount of user information to construct short‐term user preference behaviors and provides recommendations for users based on the similarity between items. We consider that an attacker uses nonprivate items to derive privacy items. Therefore, we randomly replace the original data in the same category. At the same time, the local differential privacy (LDP) is added to the privacy item query to make the private data available and protect the privacy information. In this paper, two real‐world datasets, ML‐100K and ML‐10M, are used for experiments. Experimental results show that the results of SDRM‐LDP are superior to other models.

The spatial position and interaction of drugs and their targets is the most important characteristics for understanding a drug's pharmacological effect, and it could help both in finding new and more precise treatment targets for diseases and in exploring the targeting effects of the new drugs. In this work, we develop a computational pipeline to confirm the spatial interaction relationship of the drugs and their targets and compare the drugs' efficacies based on the interaction centers. First, we produce a 100-sample set to reconstruct a stable docking model of the confirmed drug-target pairs. Second, we set 5.5 Å as the maximum distance threshold for the drug-amino acid residue atom interaction and construct 3-dimensional interaction surface models. Third, by calculating the spatial position of the 3-dimensional interaction surface center, we develop a comparison strategy for estimating the efficacy of different drug-target pairs. For the 1199 drug-target interactions of the 649 drugs and 355 targets, the drugs that have similar interaction center positions tend to have similar efficacies in disease treatment, especially in the analysis of the 37 targeted relationships between the 15 known anti-cancer drugs and 10 target molecules. Furthermore, the analysis of the unpaired anti-cancer drug and target molecules suggests that there is a potential application for discovering new drug actions using the sampling molecular docking and analyzing method. The comparison of the drug-target interaction center spatial position method better reflect the drug-target interaction situations and could support the discovery of new efficacies among the known anti-cancer drugs.

An increasing number of functional studies shows that long noncoding RNAs (lncRNAs) are involved in many aspects of cellular physiology and fulfills a wide variety of regulatory roles at almost every stage of gene expression. A major feature of lncRNAs is the highly folded modular domains in transcripts. With improved modeling and definition, it is now feasible to explore and gain novel insights into the structural-functional relationship of lncRNAs and their association with complex human diseases. In this study, we utilized an automatic computational pipeline to scan lncRNA architecture at the genome-wide scale and to obtain a landscape of functional domains. An accurate alignment algorithm was performed to identify 40 triple pairs between single-nucleotide polymorphisms (SNPs), lncRNAs and diseases. In order to detect the potential contribution of a lncRNA's modular character, we estimated and evaluated structural rearrangements, which were derived from disease-associated SNPs. In addition, we focused on annotating and comparing the global and local heterogeneity of the wild-type and mutant lncRNAs. Assessing lncRNA architecture has yielded how variations in structured regions impact the molecular mechanisms of lncRNAs and how SNPs disturb binding and recruiting ability. These observations are the first glimpse of the 'lncRNA structurome' and make it possible to robustly explore and assemble intricate space conformation and their stress variation. This result also successfully demonstrates that lncRNA transcripts contain a complex structural landscape and highlights the proposed contribution of lncRNA structure in controlling RNA functions and disease mechanisms.