Entropy-Based Privacy against Profiling of User Mobility
Entropy-Based Privacy against Profiling of User Mobility
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DOI：
10.3390/e17063913
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发表时间：
2015-06
期刊：
影响因子：
2.7
通讯作者：
Alicia Rodriguez-Carrion;David Rebollo-Monedero;J. Forné;Celeste Campo;C. García-Rubio;Javier Parra-Arnau;Sajal K. Das
中科院分区：
文献类型：
--
作者：
Alicia Rodriguez-Carrion;David Rebollo-Monedero;J. Forné;Celeste Campo;C. García-Rubio;Javier Parra-Arnau;Sajal K. Das
如今，基于位置的服务（LBS）充斥着移动的手机，但它们的使用带来了明显的隐私风险。LBS提供商可以利用伴随LBS查询的位置来构建访问位置的用户配置文件，这可能会泄露敏感数据，例如工作或家庭位置。熵的经典概念被广泛用于评估这些场景中的隐私，其中信息表示为分类数据的独立样本序列。然而，由于LBS查询可能被非常频繁地发送，因此位置简档可以通过添加时间依赖性来改进，从而成为移动性简档，其中位置样本不再独立并且可能公开用户的移动性模式。由于考虑了时间维度，经典的熵概念福尔斯达不到评估真实的隐私水平，这也取决于时间分量。因此，我们建议将基于熵的隐私度量扩展到使用熵率来评估移动配置文件。然后，两个扰动机制被认为是保持位置和移动性配置文件下逐渐效用约束。我们进一步使用建议的隐私度量，并将其与经典的评估合成和真实的移动配置文件时，提出的扰动方法。结果证明了所提出的度量的有用性的移动配置文件和需要量身定制的扰动方法的移动配置文件的功能，以提高隐私，而不完全失去效用。
Location-based services (LBSs) flood mobile phones nowadays, but their use poses an evident privacy risk. The locations accompanying the LBS queries can be exploited by the LBS provider to build the user profile of visited locations, which might disclose sensitive data, such as work or home locations. The classic concept of entropy is widely used to evaluate privacy in these scenarios, where the information is represented as a sequence of independent samples of categorized data. However, since the LBS queries might be sent very frequently, location profiles can be improved by adding temporal dependencies, thus becoming mobility profiles, where location samples are not independent anymore and might disclose the user’s mobility patterns. Since the time dimension is factored in, the classic entropy concept falls short of evaluating the real privacy level, which depends also on the time component. Therefore, we propose to extend the entropy-based privacy metric to the use of the entropy rate to evaluate mobility profiles. Then, two perturbative mechanisms are considered to preserve locations and mobility profiles under gradual utility constraints. We further use the proposed privacy metric and compare it to classic ones to evaluate both synthetic and real mobility profiles when the perturbative methods proposed are applied. The results prove the usefulness of the proposed metric for mobility profiles and the need for tailoring the perturbative methods to the features of mobility profiles in order to improve privacy without completely loosing utility.