A Location-Based Service (LBS) refers to geolocation-based services that bring both convenience and vulnerability. With an increase in the scale and value of data, most existing location privacy protection protocols cannot balance privacy and utility. To solve the revealing problems in LBS, we propose a differential privacy protection protocol based on location entropy. First, we design an algorithm of the best-assisted user selection for constructing anonymity sets. Second, we employ smart contracts to evaluate the credibility of participants, which ensures the honesty of participants. Moreover, we provide a comprehensive experiment; the theoretical analysis and experiments show that the proposed protocol effectively resists background knowledge attacks. Generally, our protocol improves data availability. Particularly, it realizes user-controllable privacy protection, which improves privacy protection and strengthens security.

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As students in online courses usually show differences in their cognitive levels and lack communication with teachers, it is difficult for teachers to grasp student perceptions of the importance of knowledge-points and to develop personalized teaching. Though recent studies have paid attention to this topic, existing methods fail to calculate the importance of every knowledge-point for each student. Moreover, some studies are based on expert analysis, are not data-driven, and hence are inapplicable to large-scale online scenarios. To address these issues, this article proposes a personal topic rank (PTR) as a solution, which links students and concepts to generate a personalized knowledge concept map. Then, the authors present a novel PTR method to calculate the importance of knowledge-points, wherein student mastery of knowledge-points, student understanding, and the knowledge-point itself are considered simultaneously. This article conducts extensive experiments on a real-world dataset to demonstrate that the method can achieve better results than baselines.