Mobile Crowdsensing is an emerging and promising sensing paradigm in which sensor data can be collected by mobile users equipped with smart devices. In Mobile Crowdsensing Systems (MCS), workers bid for location-based sensing tasks and get rewards from the platform. However, the bidding may leak workers’ path privacy, which means the sensitive locations could be inferred from innocent locations along a path as workers continuously acquire for tasks. This privacy concern may significantly hinder the participation of workers. As a result, this paper designs a novel framework for adjustable path privacy preservation used for task acquisition in MCS. In this framework, workers are allowed to flexibly adjust their privacy preferences on the amount, sensitivity, and cost of private locations. Two algorithms are proposed to determine the set of bidding tasks for workers that jointly consider the privacy concerns and profits. The first algorithm processes in a centralized approach, which is proved to be rational, truthful and efficient. The second algorithm allows workers to decide their task acquisition locally, and guarantees the Nash equilibrium among workers. Both algorithms are validated via real-world dataset. The evaluation results demonstrate that the two proposed algorithms outperform baseline algorithms on both platform and worker sides.

移动人群感知是一种新兴且有前途的传感范例，其中配备智能设备的移动用户可以收集传感器数据。在移动人群感知系统（MCS）中，工作人员竞标基于位置的传感任务并从平台中获得奖励。但是，竞标可能会泄漏工人的路径隐私，这意味着当工人不断获取任务时，可以从沿路径的无辜位置推断出敏感位置。这种隐私问题可能会严重阻碍工人的参与。因此，本文设计了一种新颖的可调整路径隐私保护框架，用于MCS中的任务获取。在此框架中，允许员工根据私人场所的数量，敏感度和成本灵活地调整其隐私偏好。提出了两种算法来确定工人的投标任务集，这些任务共同考虑了隐私问题和利润。第一种算法采用集中化方法进行处理，被证明是合理，真实和有效的。第二种算法允许工人在本地决定他们的任务获取，并保证工人之间的纳什均衡。两种算法均通过实际数据集进行了验证。评估结果表明，所提出的两种算法在平台和工作人员方面均优于基线算法。并保证工人之间的纳什均衡。两种算法均通过实际数据集进行了验证。评估结果表明，所提出的两种算法在平台和工作人员方面均优于基线算法。并保证工人之间的纳什均衡。两种算法均通过实际数据集进行了验证。评估结果表明，所提出的两种算法在平台和工作人员方面均优于基线算法。