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Optimizing rewards allocation for privacy-preserving spatial crowdsourcing

机译：优化奖励分配，以保护隐私的空间众包

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摘要

Rewards allocation in one of the key issues for ensuring a high task acceptance rate in spatial crowdsourcing applications. Generally, workers who participate in a crowdsourcing project are required to disclose their locations, which may lead to serious privacy threats. Unfortunately, providing a rigid privacy guarantee is incompatible with ensuring a high task acceptance rate in most existing crowdsourcing solutions. Hence, this paper proposes a crowdsourcing framework based on optimized reward allocation strategies. The key idea is to tune the reward for performing each task to the workers' preferences to attain a high acceptance rate. The first step in the framework is to interrogate the workers' preferences using a cryptographic protocol that fully preserves the location privacy of the workers. Based on those preferences, two different approaches to reward assignments have been proposed to ensure the rewards are distributed optimally. A theoretical analysis of the privacy protection inherent in the framework demonstrates that the proposed framework guarantee the worker's location privacy from adversaries including the requester and crowdsourcing server. Further, experiments based on real-world datasets show that the proposed strategies outperform existing solutions in terms of task acceptance rates.
机译：奖励分配是确保空间众包应用程序中较高的任务接受率的关键问题之一。通常，参与众包项目的工人必须公开其位置，这可能会导致严重的隐私威胁。不幸的是，在大多数现有的众包解决方案中，提供严格的隐私保证与确保较高的任务接受率不兼容。因此，本文提出了一种基于优化奖励分配策略的众包框架。关键思想是根据工人的喜好调整执行每个任务的奖励，以达到较高的接受率。该框架的第一步是使用一种加密协议来询问工人的偏好，该协议完全保留了工人的位置隐私。基于这些偏好，提出了两种不同的奖励分配方法，以确保最佳地分配奖励。对框架中固有的隐私保护进行的理论分析表明，提出的框架可确保工人免受来自请求者和众包服务器的对手的位置隐私。此外，基于现实世界数据集的实验表明，在任务接受率方面，所提出的策略优于现有解决方案。

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