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首页> 美国卫生研究院文献>PLoS Clinical Trials >Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset
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Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset

机译：基于时空K-匿名数据集顺序规则的过渡概率矩阵构造的位置预测

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Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users’ privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction methods based on transition probability matrices constructing from sequential rules for spatial-temporal k-anonymity dataset. First, we define single-step sequential rules mined from sequential spatial-temporal k-anonymity datasets generated from continuous LBS queries for multiple users. We then construct transition probability matrices from mined single-step sequential rules, and normalize the transition probabilities in the transition matrices. Next, we regard a mobility model for an LBS requester as a stationary stochastic process and compute the n-step transition probability matrices by raising the normalized transition probability matrices to the power n. Furthermore, we propose two location prediction methods: rough prediction and accurate prediction. The former achieves the probabilities of arriving at target locations along simple paths those include only current locations, target locations and transition steps. By iteratively combining the probabilities for simple paths with n steps and the probabilities for detailed paths with n-1 steps, the latter method calculates transition probabilities for detailed paths with n steps from current locations to target locations. Finally, we conduct extensive experiments, and correctness and flexibility of our proposed algorithm have been verified.
机译：时空k匿名性已成为保护基于位置的服务（LBS）应用程序中用户隐私的技术中的主流方法，并且已应用于LBS快照查询和连续查询等多种变体。分析大规模时空匿名集可能会有益于LBS应用程序。在本文中，我们提出了两种基于过渡概率矩阵的位置预测方法，该概率矩阵是根据时空k匿名数据集的顺序规则构造而成的。首先，我们定义单步顺序规则，该规则是从针对多个用户的连续LBS查询生成的顺序时空k匿名数据集中提取的。然后，我们从挖掘的单步顺序规则构造过渡概率矩阵，并对过渡矩阵中的过渡概率进行归一化。接下来，我们将LBS请求者的移动性模型视为固定随机过程，并通过将归一化的转移概率矩阵提升为幂n来计算n步转移概率矩阵。此外，我们提出了两种位置预测方法：粗略预测和精确预测。前者实现了沿仅包括当前位置，目标位置和过渡步骤的简单路径到达目标位置的可能性。通过将具有n步的简单路径的概率和具有n-1步的详细路径的概率进行迭代组合，后一种方法计算具有n步的详细路径的从当前位置到目标位置的转移概率。最后，我们进行了广泛的实验，并验证了所提出算法的正确性和灵活性。

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