Location prediction acts as a fundamental service in today's location-based information platform, which helps users access locations satisfying their demands, improving both user experience and platform profit. Since users with unambiguous demands prefer specific locations while users with compound demands consider first regions and then specific locations, it is necessary to model multi-grained user preferences at different geographical scales. However, most of the existing works concentrate on user preferences at the location-scale only, which can not understand users traveling behaviors thoroughly. In this paper, we propose to model both the fine-grained user preferences at the location scale and the coarsegrained user preferences at the region scale. Specifically, the proposed model harnesses the efficient information extraction power of graph neural networks. Moreover, the proposed geographical calibration method also helps to capture multi-grained user preferences accurately. Experiments on datasets of two very large cities demonstrate the significant performance improvement using our approach over state-of-the-art models. We also conduct experiments to further demonstrate the effectiveness of each component in the proposed model. Source codes of this paper are available at https://github.com/tsinghua-fib-lab/SIGSPATIAL-MMGUP/.

With the increasing diversity of mobile apps, users install many apps in their smartphones and often use several apps together to meet a specific requirement. Because of the evolution of user habits and app functions, the set of apps using at the same time, i.e., app usage context, may change over time, which represents the dynamic correlation of different apps and even the evolution trend of the whole app ecosystem. Therefore, understanding how an apps usage context changes over time is very meaningful. In this paper, based on a seven-year app usage dataset, we explore the long-term app usage context dynamics and understand the underlying reasons and influence factors behind. Specifically, we build app co-occurrence graphs in different periods and learn app embeddings accordingly by leveraging graph embedding algorithm. We then measure the change of app usage context by the distance between neighboring app embeddings. As for the whole app ecosystem, we find that the change rate of app usage context undergoes up and down phrases, and varies in different app-categories. Furthermore, we explore three influence factors correlated with such dynamics. These results will be helpful for stakeholders to better understand the evolution of mobile users app usage behavior.

To realize the great potential of the intelligent transportation infrastructure, the investment in the transportation infrastructure in the intelligent transportation system should be rationally planned. Firstly, the application status of cutting-edge Data Envelopment Analysis (DEA) model in transportation infrastructure efficiency evaluation is analyzed, and based on this, a DEA model of transportation infrastructure efficiency evaluation under Digital Twins technology is established. Secondly, with the transportation infrastructure of 12 prefecture-level cities in Jiangsu Province from 2005 to 2020 as the research object, the Digital Twins DEA model and the traditional Stochastic Frontier Approach (SFA) model are used to estimate the efficiency of transportation infrastructure in 12 cities. Finally, the traffic flow data of a certain road section in Zhenjiang City (J11 City) is simulated and predicted by using the Long Short-term Memory (LSTM) traffic flow prediction model. The results show that the average efficiency of the 12 cities estimated by the DEA model based on the Digital Twins is 0.7083, the average efficiency of the 12 cities estimated by the SFA model is 0.6445, and there are significant differences in the efficiency rankings of the cities. Compared with the actual efficiency, the established Digital Twins DEA model is more reasonable for the calculation of transportation infrastructure efficiency. The results of the LSTM traffic flow prediction model show that the Mean Absolute Error (MAE) of the LSTM model is 24.29, the Root Mean Square Error (RSME) is 0.1186, and the Mean Absolute Perce (MAPE) is 17.78, which are all lower than other models. Compared with other models, the proposed LSTM-based traffic flow prediction model is more accurate in traffic flow prediction. Hence, the research content provides a reference for the investment planning of intelligent transportation system infrastructure.

With the flourishing of location based social networks, posting check-ins has become a common practice to document one's daily life. Users usually do not consider check-in records as violations of their privacy. However, through analyzing two real-world check-in datasets, our study shows that check-in records are vulnerable to linkage attacks. Specifically, adversary is able to uniquely re-identify over 52~66 percent users in other anonymous mobility datasets and 60~80 percent users have more than 60 percent probability leaking unreported mobility records. In addition, we further demonstrate that the privacy sensitivity of check-in records can be more accurately measured by including the information of additional mobility data compared with only looking at check-ins. Based on this observation, we design a partition-and-group framework to integrate the information of check-ins and additional mobility data to attain a novel privacy criterion-kτ,l-anonymity. It ensures adversaries with arbitrary background knowledge cannot use check-ins to re-identify users in other anonymous datasets or learning unreported mobility records. The proposed framework achieves favorable performance against state-of-art baseline in terms of improving check-in utility by 24~57 percent while providing stronger privacy guarantee at the same time. We believe this study will open a new angle in attaining both privacy-preserving and useful check-in services.services.

The diversity of personal interest and preference of mobile users results in a wide spectrum of mobile app usage, and it is important to predict such app preference in order to provide personalized services. However, currently available individual app usage data is very limited, which does not cover a large user base. In this paper, we demonstrate that it is possible to make personalized app usage estimation by learning user's app preference from the social media, i.e., public accessible tweets, which can also reflect user's interest and make up for the sparsity of app usage data. By proposing a novel generative model named IMCF+ to transfer user interest from rich tweet information to sparse app usage, we achieve personalized app recommendations via learning the interest's correlation between apps and tweets. Based on a real-world app usage and tweet dataset over a large population, we evaluate the performance of IMCF+ with a variety of scenarios and parameters. With only 10 percent training data, our IMCF+ approach achieves 82.5 percent hitrate in predicting the top ten apps. Moreover, IMCF+ outperforms the other six state-of-the-art algorithms by 4.7 percent and 10 percent in high sparsity case and user cold-start scenario, indicating the effectiveness of our method. All these results demonstrate that our technique can reliably learn user's interest from tweets to help solve the personalized app recommendation problem. Our study is the first step forward for transferring user's interest learned from social media to app preference, which paves the way for providing higher-quality personalized app recommendation and services for mobile users.

Large scale cellular network accessing records are generated by mobile users on daily basis, which leave fine-grained footprints that have potential to compromise the privacy of user mobility. Abundant previous researches have demonstrated that simple anonymization has limited effect in preserving user's privacy due to prevalence of re-identification attacks. As a result, the mobile operators and application vendors usually turn to a more aggressive solution that is removing the identifier (ID) of each entry in the records. Cellular data sets owners believe that such procedure is sufficient for preserving user's privacy, since the attackers cannot directly put together the cellular records that belong to one individual, let alone recover user's identity. However, in this paper, we argue and prove that simply removing the IDs is not sufficient for preserving mobile users' privacy. We develop a mechanism that is able to extract the mobility patterns of users from cellular records and associate ID-removed records belonging to same individuals. Extensive experiments show that 70%~80% records of each user on average can be accurately recovered for two data sets collected from both mobile application and mobile operator side at the scale of several thousands to tens of thousands users. We find that the number of users released, the temporal and spatial granularity, and the speed of user movement are key factors that determine the privacy leakage in the ID-removed cellular data sets.

With the flourishing of location based social networks, posting check-ins has become a common practice to document one's daily life. Users usually do not consider check-in records as violations of their privacy. However, through analyzing two real-world check-in datasets, our study shows that check-in records are vulnerable to linkage attacks. To address this problem, we design a partition-and-group framework to integrate the information of check-ins and additional mobility data to attain a novel privacy criterion - kt, l-anonymity. It ensures adversaries with arbitrary background knowledge cannot use check-ins to re-identify users in other anonymous datasets or learning unreported mobility records. The proposed framework achieves favorable performance against state-of-art baseline in terms of improving check-in utility by 24% ~ 57% while providing stronger privacy guarantee at the same time. We believe this study will open a new angle in attaining both privacy-preserving and useful check-in services.

Nowadays, human trajectories are widely collected and utilized for scientific research and business purpose. However, publishing trajectory data without proper handling might cause severe privacy leakage. A large body of works is dedicated to merging one’s trajectory with others’, so as to avoid any individual trajectory being re-identified. Yet their solutions do not provide enough protection since they cannot prevent semantic attack, which means the attackers are able to acquire individual’s private information by using the semantics features of frequently visited locations in the trajectory even without re-identification. In this paper, we are the first to recognize the semantic attack, which is another severe privacy problem in publishing trajectory datasets. We propose an algorithm providing strong privacy protection against both the semantic and re-identification attack while reserving high data utility. Extensive evaluations based on two real-world datasets demonstrate that our solution improves the quality of privacy protection by three times, sacrificing only 36% and 10% of spatial and temporal resolution, respectively.

Trajectory data has been widely collected via mobile devices and publicly released for academic research and commercial purposes. One primary concern of publishing such a dataset is the privacy issue. Previous protection schemes mainly focus on preventing re-identification attack, which utilizes the uniqueness of trajectories. However, the correlation between trajectories, which has not been given much attention to before, could also give rise to serious privacy leakage. Recent studies have proved that it is possible to identify social relationship, de-anonymize trajectories or even infer user’s locations by analyzing the correlation between users’ trajectories. We identify the serious privacy problem of social relationship leakage caused by what we call social relationship attack and aim to protect social relationship information, which cannot be protected by existing algorithms. We contribute to the design of a new privacy model and an effective system to deal with social relationship attack and re-identification attack simultaneously while maintaining high data utility. We propose aSlidingWindowalgorithm to merge trajectories according to theirsocial-aware distance, which concerns both the spatiotemporal distance and social proximity. Evaluations of two trajectory datasets under different scenarios demonstrate that our system provides more than 1.84 times privacy protection at the cost of only 2.5% data utility loss.

Nowadays, human trajectories are widely collected and utilized for scientific research and business purpose. However, publishing trajectory data without proper handling might cause severe privacy leakage. A large body of works are dedicated to merging one's trajectory with others', so as to avoid any individual trajectory being re-identified. Yet their solutions do not provide enough protection since they cannot prevent semantic attack, which means the attackers are able to acquire individual's private information by using the semantics features of frequently- visited locations in the trajectory even without re- identification. In this work, we are the first to recognize the semantic attack, which is another severe privacy problem in publishing trajectory datasets. We propose an algorithm providing strong privacy protection against both the semantic and re- identification attack while reserving high data utility. Extensive evaluations based on two real-world datasets demonstrate that our solution improves the quality of privacy protection by 3 times, sacrificing only 36% and 30% of spatial and temporal resolution, respectively.

Human mobility data has been ubiquitously collected through cellular networks and mobile applications, and publicly released for academic research and commercial purposes for the last decade. Since releasing individual's mobility records usually gives rise to privacy issues, datasets owners tend to only publish aggregated mobility data, such as the number of users covered by a cellular tower at a specific timestamp, which is believed to be sufficient for preserving users' privacy. However, in this paper, we argue and prove that even publishing aggregated mobility data could lead to privacy breach in individuals' trajectories. We develop an attack system that is able to exploit the uniqueness and regularity of human mobility to recover individual's trajectories from the aggregated mobility data without any prior knowledge. By conducting experiments on two real-world datasets collected from both mobile application and cellular network, we reveal that the attack system is able to recover users' trajectories with accuracy about 73%~91% at the scale of tens of thousands to hundreds of thousands users, which indicates severe privacy leakage in such datasets. Through the investigation on aggregated mobility data, our work recognizes a novel privacy problem in publishing statistic data, which appeals for immediate attentions from both academy and industry.