Generative Models for Simulating Mobility Trajectories
Generative Models for Simulating Mobility Trajectories
复制标题
用于模拟移动轨迹的生成模型
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
B. Garbinato
中科院分区：
文献类型：
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作者：
Vaibhav Kulkarni;Natasa Tagasovska;Thibault Vatter;B. Garbinato
移动数据集是评估地理信息系统算法和促进实验再现性的基础。但隐私问题限制了这些数据集的共享，因为即使是聚合的位置数据也容易受到成员推断攻击。当前的合成移动性数据集生成器试图表面上匹配先验建模的移动性特征，其不准确地反映真实世界的特征。因此，对人类移动性进行建模以生成合成的但在语义和统计上真实的轨迹对于发布具有令人满意的效用水平的轨迹数据集同时保护用户隐私是至关重要的。具体来说，人类移动性固有的长期依赖性很难用判别模型和生成模型来捕捉。在本文中，我们对递归神经架构（RNN），生成对抗网络（GAN）和非参数copula的性能进行了基准测试，以生成合成的移动性轨迹。我们评估生成的轨迹，其地理和语义相似性，昼夜节律，远程依赖性，训练和生成时间。我们还包括两个样本测试，以评估所观察到的和模拟的分布之间的统计相似性，我们分析的隐私权衡成员推断和位置序列攻击。
Mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems and facilitating experimental reproducibility. But privacy implications restrict sharing such datasets, as even aggregated location-data is vulnerable to membership inference attacks. Current synthetic mobility dataset generators attempt to superficially match a priori modeled mobility characteristics which do not accurately reflect the real-world characteristics. Modeling human mobility to generate synthetic yet semantically and statistically realistic trajectories is therefore crucial for publishing trajectory datasets having satisfactory utility level while preserving user privacy. Specifically, long-range dependencies inherent to human mobility are challenging to capture with both discriminative and generative models. In this paper, we benchmark the performance of recurrent neural architectures (RNNs), generative adversarial networks (GANs) and nonparametric copulas to generate synthetic mobility traces. We evaluate the generated trajectories with respect to their geographic and semantic similarity, circadian rhythms, long-range dependencies, training and generation time. We also include two sample tests to assess statistical similarity between the observed and simulated distributions, and we analyze the privacy tradeoffs with respect to membership inference and location-sequence attacks.