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首页> 外文期刊>Network and Service Management, IEEE Transactions on >Walking Without Friends: Publishing Anonymized Trajectory Dataset Without Leaking Social Relationships
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Walking Without Friends: Publishing Anonymized Trajectory Dataset Without Leaking Social Relationships

机译：没有朋友同行：在不泄露社会关系的情况下发布匿名的轨迹数据集

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摘要

Trajectory data has been widely collected via mobile devices and publicly released for academic research and commercial purposes. One primary concern of publishing such a dataset is the privacy issue. Previous protection schemes mainly focus on preventing re-identification attack, which utilizes the uniqueness of trajectories. However, the correlation between trajectories, which has not been given much attention to before, could also give rise to serious privacy leakage. Recent studies have proved that it is possible to identify social relationship, de-anonymize trajectories or even infer user's locations by analyzing the correlation between users' trajectories. We identify the serious privacy problem of social relationship leakage caused by what we call social relationship attack and aim to protect social relationship information, which cannot be protected by existing algorithms. We contribute to the design of a new privacy model and an effective system to deal with social relationship attack and re-identification attack simultaneously while maintaining high data utility. We propose a Sliding Window algorithm to merge trajectories according to their social-aware distance, which concerns both the spatiotemporal distance and social proximity. Evaluations of two trajectory datasets under different scenarios demonstrate that our system provides more than 1.84 times privacy protection at the cost of only 2.5% data utility loss.
机译：轨迹数据已通过移动设备广泛收集并公开发布以用于学术研究和商业目的。发布此类数据集的一个主要问题是隐私问题。先前的保护方案主要集中在利用轨迹的唯一性来防止重新识别攻击。但是，轨迹之间的相关性以前没有得到足够的重视，也可能导致严重的隐私泄露。最近的研究证明，通过分析用户轨迹之间的相关性，可以识别社交关系，对轨迹进行匿名处理甚至推断用户的位置。我们确定了由所谓的社交关系攻击引起的社交关系泄漏的严重隐私问题，并旨在保护无法由现有算法保护的社交关系信息。我们致力于设计新的隐私模型和有效的系统，以同时处理社交关系攻击和重新识别攻击，同时保持较高的数据实用性。我们提出了一种滑动窗算法，根据轨迹的社会意识距离来合并轨迹，该轨迹既涉及时空距离，又涉及社交距离。对不同情况下的两个轨迹数据集的评估表明，我们的系统提供了超过1.84倍的隐私保护，而数据公用事业损失仅为2.5％。

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  • 作者

    Zhao Kai; Tu Zhen; Xu Fengli; Li Yong; Zhang Pengyu; Pei Dan; Su Li; Jin Depeng;

  • 作者单位

    Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China;

    Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China;

    Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China;

    Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China;

    Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China;

    Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China;

    Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China;

    Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China;

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  • 正文语种 eng
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  • 关键词

    Privacy preserving data publishing; privacy; trajectory; social relationship;

    机译：隐私保留数据出版;隐私;轨迹;社会关系;

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