Protecting location privacy in online and offline contexts

基于位置的服务(LBSS)的出现伴随着移动数据的大规模收集，LBSS根据用户的位置(例如地理位置搜索)对提供的信息进行个性化处理。一方面，这些流动数据集具有很高的科学、社会和经济价值。另一方面，了解一个人的位置是对他/她隐私的最大威胁之一，因为它具有很强的推理潜力和获得丰富个人信息的可能性。特别是在过去，我设计了推理攻击，利用用户的位置数据来推断其他个人信息(如表征其移动性的兴趣点)，预测他/她未来的行动，甚至执行去匿名化攻击。*我的研究项目的范围包括两种不同的环境，用户的位置隐私应该受到保护。第一上下文对应于用户在线的情况(即，当他/她实时受益于基于位置的服务时)。在这种情况下，我建议研究两种不同的方法，其目标是使隐私保护的LBS能够在最小化信任假设的情况下运行：本地计算方法和合作方法。考虑的第二个上下文是离线设置，其中已经收集了数千用户的位置数据，并且在其被发布之前(例如，在打开或共享该数据之前)必须对其进行净化。更准确地说，在我的发现拨款期间，我提议致力于流动性挖掘的净化方法的设计，其目标是产生一种数据结构，可以用来推导出人群的通用流动性模式，同时隐藏个人的流动。最后，在基本层面上，我对如何以一种对需要评估处理、共享和收集位置数据的隐私风险的从业者既有意义又有用的方式对位置隐私进行建模和量化非常感兴趣。因此，我建议研究如何在现有的位置隐私模型中整合语义维度。*我的研究项目成果的社会影响可能很重要，因为它们有可能显著改善LBSS用户的隐私状况。此外，开发的解决方案将通过帮助加拿大公司实施隐私保护LBS来发挥推动作用。特别是，一个重大的社会和经济挑战是促进LBS的发展，同时提供足够的隐私保障。因此，必须开发隐私保护LBS，以避免大数据转变为老大哥，而我的研究项目的结果将直接有助于这一点。最后，所进行的研究将与HQP(即博士和硕士研究生)的形成合作并为其做出贡献。

The advent of Location-Based Services (LBSs), which personalize the information provided according to the position of their users (e.g., geolocated search), has been accompanied by the large-scale collection of their mobility data. On the one hand, these mobility datasets have a high scientific, societal and economical value. On the other hand, learning the location of an individual is one of the greatest threats against his/her privacy due to its strong inference potential and the possibility of deriving a wealth of personal information. In particular in the past, I have designed inference attacks that use the location data of a user to deduce other personal information (such as the points of interests characterizing his/her mobility), to predict his/her future movements or even to perform a de-anonymization attack.******The scope of my research program covers two different contexts in which the location privacy of a user should be protected. The first context corresponds to the situation in which the user is online (i.e., when he/she benefits from a location-based service in real-time). In this setting, I propose to investigate two different approaches whose objective is to enable privacy-preserving LBSs to operate while minimizing the trust assumptions: the local computation approach and the cooperative one. The second context considered is the offline setting, in which the location data of thousands of users has been collected and has to be sanitized before it is released (e.g., before opening or sharing this data). More precisely, during my discovery grant I propose to work on the design of sanitization methods for mobility mining, whose objective is to produce a data structure that can be used to derive generic mobility patterns of the population while hiding individual movements. Finally at the fundamental level, I am deeply interested in how to model and quantify location privacy in a manner that is both meaningful and useful for practitioners who need to assess the privacy risks of processing, sharing and collecting location data. Thus I propose to study how to integrate the semantic dimension in the currently existing location privacy models. ******The societal impact of my research program's outcomes can be important, as they have the potential to improve significantly the privacy situation of users of LBSs. In addition, the solutions developed will act as enablers by helping Canadian companies to implement privacy-preserving LBS. In particular, a major social and economic challenge is to foster the development of LBS while providing sufficient privacy guarantees. Thus, privacy-preserving LBS have to be developed to avoid the transformation of Big Data into Big Brother, and the results of my research program will directly contribute to this. Finally, the research conducted will be done in cooperation with and contribute to the formation of HQP (i.e., PhD and master students).