College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Sensors (Basel). 2017 Sep 4;17(9):2024. doi: 10.3390/s17092024.
Incentive mechanisms of crowdsensing have recently been intensively explored. Most of these mechanisms mainly focus on the standard economical goals like truthfulness and utility maximization. However, enormous privacy and security challenges need to be faced directly in real-life environments, such as cost privacies. In this paper, we investigate offline verifiable privacy-protection crowdsensing issues. We firstly present a general verifiable privacy-protection incentive mechanism for the offline homogeneous and heterogeneous sensing job model. In addition, we also propose a more complex verifiable privacy-protection incentive mechanism for the offline submodular sensing job model. The two mechanisms not only explore the private protection issues of users and platform, but also ensure the verifiable correctness of payments between platform and users. Finally, we demonstrate that the two mechanisms satisfy privacy-protection, verifiable correctness of payments and the same revenue as the generic one without privacy protection. Our experiments also validate that the two mechanisms are both scalable and efficient, and applicable for mobile devices in crowdsensing applications based on auctions, where the main incentive for the user is the remuneration.
近年来，众包感知的激励机制受到了广泛的关注。这些机制主要集中在标准的经济目标上，如真实性和效用最大化。然而，在现实环境中，需要直接面对巨大的隐私和安全挑战，例如成本隐私。在本文中，我们研究了离线可验证的隐私保护众包感知问题。我们首先为离线同质和异质感知任务模型提出了一个通用的可验证隐私保护激励机制。此外，我们还为离线子模感知任务模型提出了一个更复杂的可验证隐私保护激励机制。这两个机制不仅探讨了用户和平台的隐私保护问题，还保证了平台和用户之间支付的可验证正确性。最后，我们证明了这两个机制在满足隐私保护、支付的可验证正确性和与没有隐私保护的通用机制相同的收益方面都是有效的。我们的实验还验证了这两个机制都是可扩展的和高效的，并且适用于基于拍卖的众包感知应用程序中的移动设备，其中用户的主要激励是报酬。
