The prosperity of location-based social networking has paved the way for new applications of group-based activity planning and marketing. While such applications heavily rely on geo-social group queries (GSGQs), existing studies fail to produce a cohesive group in terms of user acquaintance. In this paper, we propose a new family of GSGQs with minimum acquaintance constraints. They are more appealing to users as they guarantee a worst-case acquaintance level in the result group. For efficient processing of GSGQs on large location-based social networks, we devise two social-aware spatial index structures, namely SaRtree and SaR*-tree. The latter improves on the former by considering both spatial and social distances when clustering objects. Based on SaR-tree and SaR*-tree, novel algorithms are developed to process various GSGQs. Extensive experiments on real datasets Gowalla and Twitter show that our proposed methods substantially outperform the baseline algorithms under various system settings.

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This study aimed to examine the relationship between information overload and individual state anxiety in the period of regular epidemic prevention and control and mediating effect of risk perception and positive coping styles. Further, we explored the moderating role of resilience. 847 Chinese participated in and completed measures of information overload, risk perception, positive coping styles, state anxiety, and resilience. The results of the analysis showed that information overload significantly predicted the level of individual state anxiety (β = 0.27, p < 0.001). Risk perception partially mediate the relationship between information overload and state anxiety (B = 0.08, 95%CI = [0.05, 0.11]) and positive coping styles also partially mediate the relationship between information overload and state anxiety(B = -0.14, 95%CI = [-0.18, -0.10]). In addition, resilience moderated the mediating effects of risk perception (β = -0.07, p < 0.05) and positive coping styles (β = -0.19, p < 0.001). Resilience also moderated the effect of information overload on state anxiety (β = -0.13, p < 0.001). These results offer positive significance for understanding the internal mechanism of the influence of information overload on individual state anxiety in the epidemic environment and shed light on how to reduce people’s state anxiety during an epidemic.

With recent advances in data-as-a-service (DaaS) and cloud computing, aggregate query services over set-valued data are becoming widely available for business intelligence that drives decision making. However, as the service provider is often a third-party delegate of the data owner, the integrity of the query results cannot be guaranteed and is thus imperative to be authenticated. Unfortunately, existing query authentication techniques either do not work for set-valued data or they lack data confidentiality. In this paper, we propose authenticated aggregate queries over set-valued data that not only ensure the integrity of query results but also preserve the confidentiality of source data. As many aggregate queries are composed of multiset operations such as set union and subset, we first develop a family of privacy-preserving authentication protocols for primitive multiset operations. Using these protocols as building blocks, we present a privacy-preserving authentication framework for various aggregate queries and further optimize their authentication performance. Security analysis and empirical evaluation show that our proposed privacy-preserving authentication techniques are feasible and robust under a wide range of system workloads.

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