A Succinct Model for Re-identification of Mobility Traces Based on Small Training Data
A Succinct Model for Re-identification of Mobility Traces Based on Small Training Data
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DOI：
10.23919/isita.2018.8664346
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Takao Murakami
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文献类型：
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作者：
Takao Murakami
基于马尔可夫链模型的移动轨迹重新识别已被广泛研究，以了解位置隐私的风险。众所周知，当训练数据量很大时，该模型可以以非常高的精度重新识别痕迹。然而，在实践中，训练数据量可能非常小，因为用户通常只向公众公开少量位置。这种情况下最先进的方法是通过张量分解训练马尔可夫链模型（转移矩阵）。之前的工作表明，即使训练数据量非常小，该方法也优于随机猜测。在本文中，我们提出了一种简洁的重新识别模型，其性能优于上述最先进的方法。我们提出的方法并不对转换模式进行建模（与马尔可夫链模型不同），而是通过矩阵分解对位于每个区域的概率进行建模。然后，它根据两个概率分布之间的 JS (Jensen-Shannon) 散度重新识别迹线。我们使用 Gowalla 数据集评估所提出的方法，并证明所提出的方法显着优于基于张量分解的马尔可夫链模型。我们还证明，即使每个用户只有一个位置可用作训练数据，所提出的方法也显着优于随机猜测。
Re-identification of mobility traces based on the Markov chain model has been widely studied to understand the risk of location privacy. It is well known that this model can re-identify the traces with very high accuracy when the amount of training data is large. However, the amount of training data can be very small in practice, since a user generally discloses only a small number of locations to the public. A state-of-the-art method in this scenario is to train the Markov chain model (transition matrices) via tensor factorization. The previous work has shown that this method outperforms a random guess even when the amount of training data is very small.In this paper, we propose a succinct model for re-identification that outperforms the state-of-the-art method explained above. Our proposed method does not model a transition pattern (unlike the Markov chain model) but models a probability of being located in each region via matrix factorization. Then it re-identifies traces based on the JS (Jensen-Shannon) divergence between two probability distributions. We evaluate the proposed method using the Gowalla dataset, and demonstrate that the proposed method significantly outperforms the tensor factorization-based Markov chain model. We also demonstrate that the proposed method significantly outperforms a random guess even when only one single location is available per user as training data.