Mobile Crowdsensing (MCS) has evolved into an effective and valuable paradigm to engage mobile users to sense and collect urban-scale information. However, users risk their location privacy while reporting data with actual sensing locations. Existing works of location privacy-preserving are primarily based on single-region location information, which rely on a trusted and centralized sensing platform and ignore the impact of regional differences on user privacy-preserving demands. To tackle this issue, we propose a Location Difference-Based Privacy-Preserving Framework (LDPF), leveraging the powerful edge servers deployed between users and the sensing platform to hide and manage users according to regional user characteristics. More specifically, for popular regions, based on the edge servers and the k-anonymity algorithm, we propose a Coordinate Transformation and Bit Commitment (CTBC) privacy-preserving method that effectively guarantees the privacy of location data without relying on a trusted sensing platform. For remote regions, based on a more realistic distance calculation mode, we design a Paillier Encryption Data Coding (PDC) privacy-preserving method that realizes the secure computation for users’ location and prevents malicious users from deceiving. The theoretical analysis and simulation results demonstrate the security and efficiency of the proposed framework in location difference-based privacy-preserving.

Mobile crowdsensing (MCS) is a way to use social resources to solve high-precision environmental awareness problems in real time. Publishers hope to collect as much sensed data as possible at a relatively low cost, while users want to earn more revenue at a low cost. Low-quality data will reduce the efficiency of MCS and lead to a loss of revenue. However, existing work lacks research on the selection of user revenue under the premise of ensuring data quality. In this paper, we propose a Publisher-User Evolutionary Game Model (PUEGM) and a revenue selection method to solve the evolutionary stable equilibrium problem based on non-cooperative evolutionary game theory. Firstly, the choice of user revenue is modeled as a Publisher-User Evolutionary Game Model. Secondly, based on the error-elimination decision theory, we combine a data quality assessment algorithm in the PUEGM, which aims to remove low-quality data and improve the overall quality of user data. Finally, the optimal user revenue strategy under different conditions is obtained from the evolutionary stability strategy (ESS) solution and stability analysis. In order to verify the efficiency of the proposed solutions, extensive experiments using some real data sets are conducted. The experimental results demonstrate that our proposed method has high accuracy of data quality assessment and a reasonable selection of user revenue.

As a new model of "Internet + Education", inter-university study has played a huge role in sharing university resources and providing personalized learning. However, inter-school study is still in the development stage, and there are still factors in the model that affect the improvement of teaching quality and professional ability. To this end, starting from the implementation effect of inter-school learning, taking curriculum satisfaction and professional improvement as two indicators to measure the effectiveness, and analyzing the factors affecting the implementation of curriculum in the process of inter-school learning from the four dimensions of students, teachers, courses, and platforms Then, from the perspective of feedback, suggestions and measures for optimizing the inter-school study mode, improving the quality of courses and improving the professional ability of students are put forward.

Mobile crowdsensing (MCS) offers a novel paradigm for large-scale sensing with the proliferation of smartphones. Task assignment is a critical problem in mobile crowdsensing (MCS), where service providers attempt to recruit a group of brilliant users to complete the sensing task at a limited cost. However, selecting an appropriate set of users with high quality and low cost is challenging. Existing works of task assignment ignore the data redundancy of large-scale users and the individual preference of service providers, resulting in a significant workload on the sensing platform and inaccurate assignment results. To tackle this issue, we propose a task assignment method based on user-union clustering and individual preferences, which considers the influence of clustering data quality and preference-based sensing cost. Firstly, we design a user-union clustering algorithm (UCA) by defining user similarity and setting user scale, which aims to balance user distribution, reduce data redundancy, and improve the accuracy of high-quality user aggregation. Then, we consider individual preferences of service providers and construct a preference-based task assignment algorithm (PTA) to achieve the diversified sensing cost control needs. To evaluate the performance of the proposed solutions, extensive simulations are conducted. The results demonstrate that our proposed solutions outperform the baseline algorithm, which realizes the individual preference-based task assignment under the premise of ensuring high-quality data.