Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.e., tasks for which the timing and type are known in advance) and do not protect the privacy of worker bids. In a dynamic and resource-constrained environment, tasks are often uncertain (i.e., the platform lacks a priori knowledge about the tasks) and worker bids may be vulnerable to inference attacks. This paper presents HERALD*, an incentive mechanism that addresses these issues through the use of uncertainty and hidden bids. Theoretical analysis reveals that HERALD* satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost. These properties are then corroborated through a series of evaluations.

翻译：移动群智感知（MCS）因其成本效益已成为日益流行的感知范式。该方法依赖平台在任务发布者发出请求时将任务外包给参与工人。尽管已有激励机制用于促进MCS中的广泛参与，但大多数机制仅关注静态任务（即时间与类型预先已知的任务），且未能保护工人出价的隐私。在动态且资源受限的环境中，任务通常存在不确定性（即平台缺乏关于任务的先验知识），而工人出价可能易受推断攻击。本文提出HERALD*激励机制，通过利用不确定性与隐藏出价来解决上述问题。理论分析表明，HERALD*满足一系列关键准则，包括真实性、个体理性、差分隐私、低计算复杂度及低社会成本。这些性质随后通过一系列评估得到验证。