Nowadays, location-based services (LBS) are becoming increasingly important and popular. However, many LBSs are probable to collect the location information of users, which leads to the leakage of location privacy. To address this problem, dummy-based schemes have been proposed by researchers. Nevertheless, most of them only consider semantic information instead of points of interest (POIs), so the virtual trajectories may be detected by advanced data mining techniques. Besides, some of them are offline or non-local, which are not suitable for online LBS scenarios. In this paper, we design AnotherMe, an online and local location privacy-preserving system based on virtual trajectory generation, and develop the system on Android and iOS platforms. The AnotherMe system has two main functions. One is to generate virtual users located in different cities by imitating the real user's moving pattern and mapping the real user's POIs, and the other is to generate virtual trajectories that are indistinguishable from real trajectories with the help of Amap API. Therefore, the AnotherMe system can preserve continuous location privacy, and even advanced data mining techniques are difficult to distinguish between the real trajectory and the corresponding virtual trajectory. Due to low response time and battery consumption, the AnotherMe system is practical for location privacy protection. Furthermore, experimental results show that the virtual trajectories generated by our solution are more indistinguishable from real trajectories than similar solutions, and the average recognition rate of virtual trajectories is 53.8%, which is close to random guessing (50%).

Predicting drug-target affinity (DTA) is a crucial step in the process of drug discovery. Efficient and accurate prediction of DTA would greatly reduce the time and economic cost of new drug development, which has encouraged the emergence of a large number of deep learning-based DTA prediction methods. In terms of the representation of target proteins, current methods can be classified into 1D sequence- and 2D-protein graph-based methods. However, both two approaches focused only on the inherent properties of the target protein, but neglected the broad prior knowledge regarding protein interactions that have been clearly elucidated in past decades. Aiming at the above issue, this work presents an end-to-end DTA prediction method named MSF-DTA (Multi-Source Feature Fusion-based Drug-Target Affinity). The contributions can be summarized as follows. First, MSF-DTA adopts a novel "neighboring feature"-based protein representation. Instead of utilizing only the inherent features of a target protein, MSF-DTA gathers additional information for the target protein from its biologically related "neighboring" proteins in PPI (i.e., protein-protein interaction) and SSN (i.e., sequence similarity) networks to get prior knowledge. Second, the representation was learned using an advanced graph pre-training framework, VGAE, which could not only gather node features but also learn topological connections, therefore contributing to a richer protein representation and benefiting the downstream DTA prediction task. This study provides new perspective for the DTA prediction task, and evaluation results demonstrated that MSF-DTA obtained superior performances compared to current state-of-the-art methods.

Nowadays, people pay more and more attention to privacy protection with the continuous occurrence of data breaches. In location-based services, dummy trajectory generation is a popular location privacy protection method. However, this method is used by some malicious users for benefits, which results in economic losses and the waste of resources of the locationbased services provider. For this problem, dummy trajectory identification has been proposed by researchers. Nevertheless, with the continuous development of dummy trajectory generation algorithms, the existing dummy trajectory identification methods are unsuitable. In this paper, we propose a hybrid neural network framework for dummy trajectory identification, called trajectory similarity hybrid networks (TSHN). The main idea of TSHN is to identify whether a target trajectory is a virtual trajectory according to the similarity score between historical trajectories and the target trajectory. For each historical trajectory of the user, the mobility and individual features are extracted to train TSHN. The trajectory similarity score generated by the TSHN is used to identify dummy trajectories. The experimental results show that our proposed TSHN can identify the dummy trajectory with an accuracy of 0.97, which significantly outperforms the existing dummy trajectory identification methods.