在用户移动数据中实现隐私和效用的权衡

Sophie Cerf, Vincent Primault, A. Boutet, Sonia Ben Mokhtar, R. Birke, S. Bouchenak, L. Chen, N. Marchand, B. Robu
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引用次数: 21

摘要

在基于位置的服务中利用位置信息可以通过地理环境化来改进服务效用。然而,这引起了隐私问题,因为新的知识可以从位置记录推断出来,比如用户的家庭和工作地点,或者个人习惯。尽管位置隐私保护机制(LPPMs)提供了一种解决此问题的方法,但它们通常需要手动配置,这对服务提供商和用户构成了重大挑战。此外,它们对数据隐私和效用的影响很少被评估。在本文中,我们提出了一个模型驱动的系统PULP,该系统通过选择适当的LPPM和配置来自动提供特定于用户的隐私保护,并有助于服务效用。PULP的核心是非线性模型,它可以捕获给定LPPM下每个用户的数据隐私和实用程序的复杂依赖关系,即地理不可分辨性和承诺。PULP根据用户对隐私和实用性的偏好,高效地推荐合适的LPPM和相应的配置。我们使用总共770个用户的四个真实移动跟踪来评估PULP模型的准确性及其实现每个用户隐私-效用权衡的有效性。我们广泛的实验表明,PULP保证了对位置服务的贡献,同时遵守了很大比例用户的隐私约束,并且比非基于模型的替代方案快几个数量级。
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PULP: Achieving Privacy and Utility Trade-Off in User Mobility Data
Leveraging location information in location-based services leads to improving service utility through geocontextualization. However, this raises privacy concerns as new knowledge can be inferred from location records, such as user's home and work places, or personal habits. Although Location Privacy Protection Mechanisms (LPPMs) provide a means to tackle this problem, they often require manual configuration posing significant challenges to service providers and users. Moreover, their impact on data privacy and utility is seldom assessed. In this paper, we present PULP, a model-driven system which automatically provides user-specific privacy protection and contributes to service utility via choosing adequate LPPM and configuring it. At the heart of PULP is nonlinear models that can capture the complex dependency of data privacy and utility for each individual user under given LPPM considered, i.e., Geo-Indistinguishability and Promesse. According to users' preferences on privacy and utility, PULP efficiently recommends suitable LPPM and corresponding configuration. We evaluate the accuracy of PULP's models and its effectiveness to achieve the privacy-utility trade-off per user, using four real-world mobility traces of 770 users in total. Our extensive experimentation shows that PULP ensures the contribution to location service while adhering to privacy constraints for a great percentage of users, and is orders of magnitude faster than non-model based alternatives.
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