DPDA: A Differentially Private Double Auction Scheme for Mobile Crowd Sensing
DPDA: A Differentially Private Double Auction Scheme for Mobile Crowd Sensing
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DOI：
10.1109/cns.2018.8433135
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Wenqiang Jin;Ming Li;Linke Guo;Lei Yang
中科院分区：
文献类型：
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作者：
Wenqiang Jin;Ming Li;Linke Guo;Lei Yang
移动人群感知(MCS)利用配备多传感器的普适移动设备来收集特定地理区域的丰富数据。由于激励用户参与的重要性，过去的文献中已经提出了基于拍卖的开放式MCS市场。请注意，他们的重点是实现关键的经济属性，但未能保护竞标隐私。虽然处理这一问题的方案有限，但它们只适用于单面拍卖，不适合属性差异很大的双面拍卖。本文受统一定价和代理机制的启发，提出了一种针对MCS的差异化私人双向拍卖(DPDA)方案，以保护拍卖双方的竞价隐私。此外，γ真实性、个体理性和预算平衡等传统经济属性也得到了保证。此外，我们还推导出了该方案的计算复杂度和近似最优平台收益的闭合形式。在真实数据集上进行了大量的仿真，以验证DPDA的效率和有效性。
Mobile crowd sensing (MCS) takes advantage of pervasive mobile devices that are equipped with multi-sensors to collect rich data of a certain geographic area. Because of the importance of incentivizing users to participate, auction-based open MCS markets have been proposed in past literature. Note that their focus is to achieve critical economic properties but fail to protect bid privacy. Although there are limited schemes dealing with this issue, they are designed only for single-side auctions and are unsuitable for double-side auctions whose properties are quite different. In this paper, inspired by uniform pricing and ex-ponential mechanism, we propose a differentially private double auction (DPDA) scheme for MCS to protect bid privacy for both auction sides. In addition, the traditional economic properties, such as γ-truthfulness, individual rationality and budget balance, are guaranteed as well. Besides, we derive closed forms over the computation complexity and the approximate optimal platform revenue achieved by the scheme. Extensive simulations have been conducted on real-world datasets to validate the efficiency and effectiveness of DPDA.