Frameworks for Privacy-Preserving Mobile Crowdsensing Incentive Mechanisms
Frameworks for Privacy-Preserving Mobile Crowdsensing Incentive Mechanisms
复制标题
隐私保护移动群智激励机制框架
DOI：
10.1109/tmc.2017.2780091
复制
发表时间：
2018-08
影响因子：
7.9
通讯作者：
Xue Guoliang
中科院分区：
文献类型：
--
作者：
Lin Jian;Yang Dejun;Li Ming;Xu Jia;Xue Guoliang
随着智能手机的快速增长，移动群智感知成为一种新的范式，它利用普遍存在的嵌入式传感器智能手机来有效地收集数据。人们提出了许多基于拍卖的激励机制来刺激智能手机用户参与移动群智感知应用和系统。然而，他们都没有同时考虑智能手机用户的投标隐私和社会成本。在本文中，我们设计了两个基于隐私保护的拍卖激励机制的框架，它们也实现了近似的社会成本最小化。在前者中，每个用户对其愿意执行的一组任务提交出价；在后者中，每个用户为其任务集中的每个任务提交出价。这两个框架都根据平台定义的评分函数来选择用户。作为例子，我们提出了两个评分函数，线性函数和对数函数，来实现这两个框架。我们严格证明两种提出的框架都实现了计算效率、个体理性、真实性、差异隐私和近似社会成本最小化。此外，通过对数得分函数，这两个框架在社会成本方面是渐近最优的。广泛的模拟评估了两个框架的性能，并证明我们的框架在牺牲社会成本的同时实现了投标隐私保护。
With the rapid growth of smartphones, mobile crowdsensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Many auction-based incentive mechanisms have been proposed to stimulate smartphone users to participate in the mobile crowdsensing applications and systems. However, none of them has taken into consideration both the bid privacy of smartphone users and the social cost. In this paper, we design two frameworks for privacy-preserving auction-based incentive mechanisms that also achieve approximate social cost minimization. In the former, each user submits a bid for a set of tasks it is willing to perform; in the latter, each user submits a bid for each task in its task set. Both frameworks select users based on platform-defined score functions. As examples, we propose two score functions, linear and log functions, to realize the two frameworks. We rigorously prove that both proposed frameworks achieve computational efficiency, individual rationality, truthfulness, differential privacy, and approximate social cost minimization. In addition, with log score function, the two frameworks are asymptotically optimal in terms of the social cost. Extensive simulations evaluate the performance of the two frameworks and demonstrate that our frameworks achieve bid-privacy preservation although sacrificing social cost.
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DOI：
10.1145/2892555
发表时间：
2011-11
期刊：
ACM Transactions on Economics and Computation (TEAC)
影响因子：
--
作者：
Yiling Chen;Stephen Chong;Ian A. Kash;T. Moran;S. Vadhan
通讯作者：
Yiling Chen;Stephen Chong;Ian A. Kash;T. Moran;S. Vadhan
移动众包的隐私保护、QoI 感知参与者协调
影响因子：
5.6
作者：
Song, Zheng;Ren, Ziyu;Ma, Jian;Wang, Wendong
通讯作者：
Wang, Wendong
DOI：
10.1109/infocom.2015.7218463
发表时间：
2015-08
期刊：
2015 IEEE Conference on Computer Communications (INFOCOM)
影响因子：
--
作者：
Ruihao Zhu;K. Shin
通讯作者：
Ruihao Zhu;K. Shin
DOI：
10.15783/c7qc7m
发表时间：
2014-07
期刊：
CRAWDAD wireless network data archive
影响因子：
--
作者：
L. Bracciale;Marc Bonola;Pierpaolo Loreti;G. Bianchi;R. Amici;Antonello Rabuffi
通讯作者：
L. Bracciale;Marc Bonola;Pierpaolo Loreti;G. Bianchi;R. Amici;Antonello Rabuffi
DOI：
10.1007/978-0-387-30162-4_436
发表时间：
2008
期刊：
--
影响因子：
--
作者：
Moshe Babaioff
通讯作者：
Moshe Babaioff