Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features
Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features
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DOI：
10.2478/popets-2021-0015
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发表时间：
2020-06
影响因子：
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通讯作者：
Takao Murakami;Koki Hamada;Yusuke Kawamoto;Takuma Hatano
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文献类型：
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作者：
Takao Murakami;Koki Hamada;Yusuke Kawamoto;Takuma Hatano
摘要随着位置服务(Location-Based Services，LBSS)的广泛应用，合成位置轨迹在分析空间大数据和保护用户隐私方面发挥着越来越重要的作用。特别是，保留特定于一群用户(例如，那些乘火车通勤的人、那些去购物的人)的特征的合成轨迹对于各种地理数据分析任务以及对于提供合成位置数据集是重要的。虽然位置合成器已经得到了广泛的研究，但现有的合成器不能提供足够的实用性、保密性和可扩展性，因此不适用于大规模的位置跟踪。为了解决这个问题，我们提出了一种新的位置合成器，称为隐私保护多重张量分解(PPMTF)。我们用转移计数张量和访问计数张量来模拟原始轨迹的各种统计特征。我们通过多个张量分解同时分解这两个张量，并通过后验采样来训练因子矩阵。然后，我们从重构的张量合成踪迹，并对合成的踪迹进行似是而非的否认测试。我们使用两个数据集对PPMTF进行了综合评估。实验结果表明，PPMTF保留了包括簇特定特征在内的各种统计特征，保护了用户隐私，并在实际时间内合成了大规模的位置跟踪。在相同的隐私级别下，PPMTF在实用性和可伸缩性方面也显著优于最先进的方法。
Abstract With the widespread use of LBSs (Location-based Services), synthesizing location traces plays an increasingly important role in analyzing spatial big data while protecting user privacy. In particular, a synthetic trace that preserves a feature specific to a cluster of users (e.g., those who commute by train, those who go shopping) is important for various geo-data analysis tasks and for providing a synthetic location dataset. Although location synthesizers have been widely studied, existing synthesizers do not provide su˚cient utility, privacy, or scalability, hence are not practical for large-scale location traces. To overcome this issue, we propose a novel location synthesizer called PPMTF (Privacy-Preserving Multiple Tensor Factorization). We model various statistical features of the original traces by a transition-count tensor and a visit-count tensor. We factorize these two tensors simultaneously via multiple tensor factorization, and train factor matrices via posterior sampling. Then we synthesize traces from reconstructed tensors, and perform a plausible deniability test for a synthetic trace. We comprehensively evaluate PPMTF using two datasets. Our experimental results show that PPMTF preserves various statistical features including cluster-specific features, protects user privacy, and synthesizes large-scale location traces in practical time. PPMTF also significantly outperforms the state-of-theart methods in terms of utility and scalability at the same level of privacy.