TWC: Small: Rigorous and Customizable Spatiotemporal Privacy for Location Based Applications

基于位置的应用的快速发展导致了人们对位置隐私的日益关注。当前的移动操作系统只为用户提供基本的位置访问控制--要么阻止，要么允许位置访问--这在缓解隐私威胁方面是不充分和低效的。现有的位置混淆机制大多基于句法隐私模型，没有考虑移动性，因此容易受到推理攻击。该项目的目标是为基于位置的应用程序中的个人位置共享开发严格和可定制的隐私概念和位置扰动机制，并研究它们在基于位置的查询和地理空间众包的背景下的影响。建立可供移动用户直接使用的隐私机制将最大限度地减少不必要地向基于位置的应用程序和其他可能的恶意第三方披露他们的准确位置和轨迹。通过解决时空隐私障碍，该项目有望在确保个人隐私的同时，在启用和促进采用基于位置的应用方面产生重大影响。该项目还包括一系列综合教育活动，包括黑客马拉松活动、研究生和本科生的参与以及鼓励女性和少数民族参与。该项目有几个目标：1)基于差异隐私及其变体开发严格的隐私概念，用于位置保护，同时通过移动性建模考虑用户位置的时空相关性，并研究最佳和启发式扰动机制，以实现最优效用的隐私保障；2)扩展隐私解决方案，以保护轨迹或一系列时间-位置对；3)通过策略图，考虑用户偏好、时空语义和隐私-效用权衡，开发可定制的位置和轨迹保护隐私解决方案。在预测范围查询、k近邻查询以及地理空间众包等基于位置的应用的背景下，研究了使用扰动位置的隐私效用权衡。有关该项目的更多信息，请访问http://www.mathcs.emory.edu/aims/stprivacy。

Rapid advances in location based applications are leading to increased concern about location privacy. Current mobile operating systems only provide users with rudimentary location access controls - either to block or allow location access - which are inadequate and inefficient in mitigating privacy threats. Most existing location obfuscation mechanisms are based on syntactic privacy models that do not consider mobility and are hence vulnerable to inference attacks. The goal of this project is to develop rigorous and customizable privacy notions and location perturbation mechanisms for individual location sharing in location based applications, and study their impact in the context of location based queries and geospatial crowdsourcing. Building privacy mechanisms that can be directly used by mobile users will minimize unnecessary disclosure of their precise locations and trajectories to location based applications and other possibly malicious third parties. By addressing the spatiotemporal privacy barrier, this project promises significant impact in enabling and promoting adoption of location based applications while ensuring individual privacy. The project also includes a set of integrated educational activities including hackathon events, involvement of graduate and undergraduate students, and encouragement of participation by women and minorities.The project has several objectives: 1) Develop rigorous privacy notions based on differential privacy and its variants for location protection while accounting for spatiotemporal correlations of a user's locations through mobility modeling, and to investigate optimal and heuristic perturbation mechanisms to achieve privacy guarantees with optimal utility; 2) Extend privacy solutions for protecting a trajectory or a sequence of time-location pairs; and 3) Develop customizable privacy solutions for location and trajectory protection through policy graphs, considering user preferences, spatiotemporal semantics, and privacy-utility tradeoffs. Privacy-utility tradeoffs using perturbed locations are studied in the context of location based applications such as predictive range queries and k-nearest neighbor queries as well as geospatial crowdsourcing. More information on this project can be found at http://www.mathcs.emory.edu/aims/stprivacy