With the unprecedented proliferation of mobile devices, Mobile Crowd Sensing (MCS) emerges as a promising computing paradigm which utilizes sensor-embedded smart devices to collect sensory data. Recently, a number of privacy-preserving auction-based incentive mechanisms have been proposed. However, none of them guarantees the quality of sensing data in double-side auction scenarios. In this paper, we propose a Differentially Private Double Auction With Reliability-Aware in Mobile Crowd Sensing (DPDR). Specifically, we design the incentive mechanism by employing the exponential mechanism in double-side auction to select the clearing price tuple. Moreover, to collect precise sensory data, we heuristically choose more reliable workers as candidates for each clearing price tuple. We further improve the social welfare of the mechanism by designing the utility function with less sensitivity, or adopting a more practical pricing strategy. Through theoretical analysis, we demonstrate that our mechanisms can guarantee both differential privacy and economic properties, including individual rationality, budget balance, approximate truthfulness and approximate maximal social welfare. Extensive experimental results show that the improved mechanisms can achieve better performance than DPDR in term of social welfare, and all proposed mechanisms can produce high-quality data.

随着移动设备的空前普及，移动人群感应（MCS）成为一种有前途的计算范例，它利用嵌入传感器的智能设备来收集传感数据。近来，已经提出了许多基于隐私保护的基于拍卖的激励机制。但是，它们都不能保证双面拍卖方案中传感数据的质量。在本文中，我们提出了一种在移动人群感知（DPDR）中具有可靠性意识的差分私下双拍卖。具体来说，我们通过在双面拍卖中采用指数机制来选择清算价格元组来设计激励机制。此外，为了收集精确的感官数据，我们启发式地选择更可靠的工人作为每个清算价格元组的候选人。我们通过设计敏感性较低的效用函数或采用更实用的定价策略来进一步改善该机制的社会福利。通过理论分析，我们证明了我们的机制可以保证不同的隐私和经济属性，包括个人理性，预算平衡，近似真实性和近似最大社会福利。大量的实验结果表明，在社会福利方面，改进的机制可以实现比DPDR更好的性能，并且所有提出的机制都可以产生高质量的数据。预算平衡，真实性和最大社会福利。大量的实验结果表明，在社会福利方面，改进的机制可以实现比DPDR更好的性能，并且所有提出的机制都可以产生高质量的数据。预算平衡，真实性和最大社会福利。大量的实验结果表明，在社会福利方面，改进的机制可以实现比DPDR更好的性能，并且所有提出的机制都可以产生高质量的数据。