With the rapid development of intelligent mobile terminals and communication technologies, location-based services (LBSs) have become an essential part of users’ lives. LBS providers upload and share the collected users’ location data. The more commonly used methods for location privacy protection are differential privacy and its extensions. However, the semantic information about location, which is an integral part of the location data, often contains sensitive user information. Most existing research methods have failed to pay enough attention to protecting the semantic information in the location data. To remedy this problem, two different scenarios for location semantic privacy protection methods are proposed in this paper to address single-point and continuous location queries. Simulation experiments on real social location check-in datasets, and comparison of three different privacy protection mechanisms, show that our solution demonstrates good service quality and privacy protection considering location semantics.

With the rapid spread of mobile device, technologies in mobile cloud increased quick and introduce huge volume of mobile data and computation. Human movement between POIs are recorded in mobile cloud, which indicate personalized behaviors. Most POI recommendation method in mobile cloud proposed to utilize recurrent neural networks and self-attention mechanism to discover users’ potential movement behaviors. In this paper, we propose a graph-enhanced spatio-temporal interval aware network (GESTIAN) to recommend the next POI. In GESTIAN, we propose a graph-based general pattern learning module to learn common behavior patterns based on a global trajectory flow graph to address the challenges caused by cold start. Furthermore, we propose a heterogeneous network with spatio-temporal interval aware with self-attention and gate recurrent unit to extract users’ long-term and short-term spatio-temporal dependencies, respectively. In addition, we leverage the scale between positive and negative samples by randomly sampling negative samples. We conduct extensive experiments based on two real-world check-in datasets. The experimental evaluations demonstrate that the proposed GESTIAN outperforms most challenging baselines by approximately 2%-10%, and achieves better performance over few-check-in history users.

In recent years, location-based social media has become popular, and a large number of spatiotemporal trajectory data have been generated. Although these data have significant mining value, they also pose a great threat to the privacy of users. At present, many studies have realized the privacy-preserving mechanism of location data in social media in terms of data utility and privacy preservation, but rarely have any of them considered the correlation between timestamps and geographical location. To solve this problem, in this paper, we first propose a k-anonymity-based mechanism to hide the user’s specific time segment during a single day, and then propose an optimized truncated Laplacian mechanism to add noise to each data grid (the frequency of time data) of the anonymized time distribution. The time data after secondary processing are fuzzy and uncertain, which not only protects the privacy of the user’s geographical location from the time dimension but also retains a certain value of data mining. Experiments on real datasets show that the TDP privacy-preserving model has good utility.