论文标题
地理 - 摩尔：一种多目标进化算法，具有移动众包工人的地理效果
Geo-MOEA: A Multi-Objective Evolutionary Algorithm with Geo-obfuscation for Mobile Crowdsourcing Workers
论文作者
论文摘要
移动互联网和共享经济的快速发展带来了空间众包（SC）的繁荣。 SC应用程序根据任务请求者和外包工人的报告位置信息（例如DIDI，MEITUAN和UBER）分配了各种任务。但是，SC-Servers通常是不可信的，用户位置的暴露引起了隐私问题。在本文中，我们设计了一个称为Geo-MoeA（带有地理浮游物的多目标进化算法）的框架，以保护移动网络环境中SC平台上涉及的工人的位置隐私。我们提出了一种自适应区域化的混淆方法，其推理错误范围基于地理位置可区分性（对差异隐私的强烈概念），适用于大规模位置数据和任务分配的背景。这使每个工人都能报告一个具有个性化推理错误阈值的伪安置，该伪位置可自适应地生成。此外，作为一种流行的计算智能方法，引入了MOEA，以优化SC服务可用性和隐私保护之间的权衡，同时从理论上确保保护位置设置的最一般条件为更大的搜索空间。最后，两个公共数据集上的实验结果表明，我们的地理摩尔方法可实现高达20％的服务质量损失，同时保证差异和地理距离位置隐私。
The rapid development of mobile Internet and sharing economy brings the prosperity of Spatial Crowdsourcing (SC). SC applications assign various tasks according to reported location information of task's requesters and outsourced workers (such as DiDi, MeiTuan and Uber). However, SC-servers are often untrustworthy and the exposure of users' locations raises privacy concerns. In this paper, we design a framework called Geo-MOEA (Multi-Objective Evolutionary Algorithm with Geo-obfuscation) to protect location privacy of workers involved on SC platform in mobile networks environment. We propose an adaptive regionalized obfuscation approach with inference error bounds based on geo-indistinguishability (a strong notion of differential privacy), which is suitable for the context of large-scale location data and task allocations. This enables each worker to report a pseudo-location that is adaptively generated with a personalized inference error threshold. Moreover, as a popular computational intelligence method, MOEA is introduced to optimize the trade-off between SC service availability and privacy protection while ensuring theoretically the most general condition on protection location sets for larger search space. Finally, the experimental results on two public datasets show that our Geo-MOEA approach achieves up to 20% reduction in service quality loss while guaranteeing differential and geo-distortion location privacy.
