Personalized Privacy-Preserving Task Allocation for Mobile Crowdsensing
Personalized Privacy-Preserving Task Allocation for Mobile Crowdsensing
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DOI：
10.1109/tmc.2018.2861393
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发表时间：
2019-06-01
影响因子：
7.9
通讯作者：
Qi, Hairong
中科院分区：
文献类型：
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作者：
Wang, Zhibo;Hu, Jiahui;Qi, Hairong
在移动的人群感知中，工作人员的位置信息通常是优化任务分配所必需的，但位置隐私泄露问题也引起了人们的严重关注。虽然已经提出了许多方法来保护用户的位置，在移动的众测任务分配的位置保护还没有得到很好的探讨。此外，据我们所知，没有现有的隐私保护任务分配机制可以提供个性化的位置保护，考虑不同的保护需求的工人。在本文中，我们提出了一个个性化的隐私保护任务分配框架移动的crowdsensing，可以有效地分配任务，同时提供个性化的位置隐私保护。其基本思想是，每个工作人员将模糊的距离和个人隐私级别上传到服务器，而不是其真实位置或任务距离。特别地，我们提出了一种概率赢家选择机制（PWSM），通过将每个任务分配给最接近它的概率最大的工作者来最小化来自工作者的模糊信息的总行程距离。此外，我们提出了一种Vickrey支付确定机制（VPDM），通过考虑每个赢家的移动成本和隐私级别来确定对每个赢家的适当支付。满足真实性、收益性和概率性的个体理性。在真实数据集上的大量实验证明了所提机制的有效性。
Location information of workers are usually required for optimal task allocation in mobile crowdsensing, which however raises severe concerns of location privacy leakage. Although many approaches have been proposed to protect the locations of users, the location protection for task allocation in mobile crowdsensing has not been well explored. In addition, to the best of our knowledge, none of existing privacy-preserving task allocation mechanisms can provide personalized location protection considering different protection demands of workers. In this paper, we propose a personalized privacy-preserving task allocation framework for mobile crowdsensing that can allocate tasks effectively while providing personalized location privacy protection. The basic idea is that each worker uploads the obfuscated distances and personal privacy level to the server instead of its true locations or distances to tasks. In particular, we propose a Probabilistic Winner Selection Mechanism (PWSM) to minimize the total travel distance with the obfuscated information from workers, by allocating each task to the worker who has the largest probability of being closest to it. Moreover, we propose a Vickrey Payment Determination Mechanism (VPDM) to determine the appropriate payment to each winner by considering its movement cost and privacy level, which satisfies the truthfulness, profitability, and probabilistic individual rationality. Extensive experiments on the real-world datasets demonstrate the effectiveness of the proposed mechanisms.