Location prediction attacks using tensor factorization and optimal defenses
Location prediction attacks using tensor factorization and optimal defenses
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DOI：
10.1109/bigdata.2014.7004384
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发表时间：
2014-10
期刊：
影响因子：
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通讯作者：
Takao Murakami;Hajime Watanabe
中科院分区：
文献类型：
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作者：
Takao Murakami;Hajime Watanabe
最近的研究提出了各种针对位置隐私的攻击，使用针对每个用户训练的马尔可夫链转移矩阵。然而，当用户过去只披露过少量的位置信息时，训练数据可能会非常稀疏。在本文中，我们展示了攻击者如何解决这种稀疏数据问题，以及防御者如何防御这种类型的攻击。我们的建议是双重的：1)我们提出了一种训练方法，将一组转移矩阵作为一个“张量”，并采用张量分解从少量的训练数据中鲁棒估计转移矩阵。2)重点研究了一种位置预测攻击，该攻击根据目标用户过去的位置信息预测目标用户的位置，并提出了一种区域合并方法，以最小化区域大小作为最优防御。使用出租车轨迹数据集的实验结果表明了我们的建议的有效性。我们还指出，当防御方有大数据训练转移矩阵时，我们的区域合并方法是有效的。
Recent studies have proposed various attacks against location privacy using a Markov Chain transition matrix trained for each user. However, when a user has disclosed only a small amount of location information in the past, the training data can be extremely sparse. In this paper, we show how the attacker can solve this sparse data problem, and how the defender can defend against this type of attack. Our proposal is twofold: 1) We propose a training method that regards a set of transition matrices as a “tensor”, and adopt tensor factorization to robustly estimate transition matrices from a small amount of training data. 2) We then focus on a location prediction attack, which predicts a location of a target user from a past location that he/she disclosed, and propose a region merging method to minimize the region size as an optimal defense. The experimental results using the dataset of taxi traces show the effectiveness of our proposals. We also point out that our region merging method is effective especially when the defender has Big Data to train transition matrices.