Traditional spatial crowdsourcing (SC) systems employ a centralized server platform to provide services for requesters. Such a centralized design requires powerful resource capacity and often cannot accomplish the urgent demands due to the unpredictable network latency. In order to ensure the scalability of systems and the quality of services, we study the distributed SC (DSC), where a diversity of location-relative services provided by various service providers (SPs) can deploy on edge clouds (ECs) with low time latency. Since the edge resources are limited, SPs need to compete for edge resources so as to deploy their desired SC services, and the requested resources must be allocated together to meet the demand of the service. We first design a gated recurrent unit with particle filter (GRUPF) network for SPs to predict future resource demands so as to participate in the competitions judiciously. Then, we model the competitive edge resource allocation problem between SPs and ECs as a combinatorial auction process. Due to the NP-hardness of this problem, an approximation algorithm is proposed to tackle it. Moreover, the leakage of private information such as bids may incur severe economic damage, and most existing studies usually rely on a trusted third party to provide rigorous privacy protection. Therefore, we customize a novel differentially private resource auction (DRA) mechanism, and design a bid confusion strategy based on differential privacy. Through theoretical analysis, we prove that the DRA mechanism meets some desired properties, including ϵ\epsilon -differential privacy, individual rationality, computational efficiency, and γ\gamma -truthfulness. Additionally, we corroborate the significant performances of DRA through extensive simulations on synthetic and real-world data sets.

中文翻译：

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具有差异隐私的分布式空间众包的边缘资源预测和拍卖


传统的空间众包（SC）系统采用集中式服务器平台为请求者提供服务。这种集中式的设计需要强大的资源能力，并且往往由于不可预测的网络延迟而无法满足紧急需求。为了确保系统的可扩展性和服务质量，我们研究了分布式SC（DSC），其中由不同服务提供商（SP）提供的各种位置相关服务可以以较低的时间部署在边缘云（EC）上延迟。由于边缘资源有限，SP需要竞争边缘资源来部署自己想要的SC服务，并且必须共同分配所请求的资源以满足服务需求。我们首先设计了一个带粒子滤波器的门控循环单元（GRUPF）网络，供SP预测未来的资源需求，从而明智地参与竞争。然后，我们将 SP 和 EC 之间的竞争优势资源分配问题建模为组合拍卖过程。由于该问题的 NP 难度，提出了一种近似算法来解决它。此外，投标等私人信息的泄露可能会带来严重的经济损失，而现有的大多数研究通常依赖于可信的第三方来提供严格的隐私保护。因此，我们定制了一种新颖的差分隐私资源拍卖（DRA）机制，并设计了基于差分隐私的竞价混淆策略。通过理论分析，我们证明了 DRA 机制满足一些期望的属性，包括 ϵ\epsilon -差分隐私、个体理性、计算效率和 γ\gamma -真实性。 此外，我们通过对合成数据集和真实数据集的广泛模拟证实了 DRA 的显着性能。