Group Sparsity Tensor Factorization for De-anonymization of Mobility Traces
Group Sparsity Tensor Factorization for De-anonymization of Mobility Traces
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DOI：
10.1109/trustcom.2015.427
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发表时间：
2015-08
期刊：
影响因子：
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通讯作者：
Takao Murakami;Atsunori Kanemura;H. Hino
中科院分区：
文献类型：
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作者：
Takao Murakami;Atsunori Kanemura;H. Hino
使用个性化转换矩阵的去匿名攻击被认为是将匿名痕迹与用户联系起来的最成功的方法之一。然而，由于许多用户在日常生活中仅向公众披露少量位置信息，因此对手可用的训练数据量可能非常小。本文的目的是量化这种现实情况下去匿名化的风险。为了实现这一目标，我们利用空间数据可以形成群结构的事实，并提出群稀疏张量分解来训练个性化转换矩阵，从少量训练数据中捕获空间群结构。我们将我们的训练方法应用于去匿名化攻击，并使用 Geolife 数据集对其进行评估。结果表明，使用张量分解的训练方法优于最大似然估计方法，并且通过结合组稀疏正则化进一步改进。
The de-anonymization attack using personalized transition matrices is known as one of the most successful approaches to link anonymized traces with users. However, since many users disclose only a small amount of location information to the public in their daily lives, the amount of training data available to the adversary can be very small. The aim of this paper is to quantify the risk of de-anonymization in this realistic situation. To achieve this aim, we utilize the fact that spatial data can form group structure, and propose group sparsity tensor factorization to train the personalized transition matrices that capture spatial group structure from a small amount of training data. We apply our training method to the de-anonymization attack, and evaluate it using the Geolife dataset. The results show that the training method using tensor factorization outperforms the Maximum Likelihood estimation method, and is further improved by incorporating group sparsity regularization.