Geo-MOEA：一种面向移动众包工作者的地理模糊多目标进化算法
Geo-MOEA: A Multi-Objective Evolutionary Algorithm with Geo-obfuscation for Mobile Crowdsourcing Workers
摘要：
移动互联网和共享经济的快速发展带来了空间众包的繁荣。SC应用根据任务请求者和外包工人（如滴滴、美团和Uber）上报的位置信息分配各种任务。然而，SC服务器通常不值得信赖，并且暴露用户的位置会引起隐私问题。在本文中，我们设计了一个框架称为Geo-MOEA（多目标进化算法与地理混淆），以保护在移动网络环境中的工作人员参与SC平台的位置隐私。我们提出了一种自适应的区域化混淆方法与推理误差界的基础上的地理不可侵犯性（一个强的概念，差分隐私），这是适合于大规模的位置数据和任务分配的情况下。这使得每个工作者能够报告利用个性化推断误差阈值自适应地生成的伪位置。此外，作为一种流行的计算智能方法，MOEA被引入到SC服务可用性和隐私保护之间的权衡优化，同时确保理论上的最一般的条件下保护位置集更大的搜索空间。最后，在两个公共数据集上的实验结果表明，我们的Geo-MOEA方法在保证差分和地理失真位置隐私的同时，实现了高达20%的服务质量损失减少。
英文摘要：
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.
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