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

基于位置的应用程序的快速发展导致人们越来越关注位置隐私。目前的移动操作系统只向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——这在减轻隐私威胁方面是不够的，效率也很低。大多数现有的位置混淆机制都是基于不考虑移动性的语法隐私模型，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的、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环境中的影响。建立可以被移动用户直接使用的隐私机制，将把他们的精确位置和轨迹不必要地泄露给基于位置的应用程序和其他可能恶意的第三方。通过解决时空隐私障碍，该项目承诺在确保个人隐私的同时，在启用和促进基于位置的应用程序的采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，以及鼓励妇女和少数民族的参与。该项目有几个目标：1)通过移动建模，在考虑用户位置的时空相关性的同时，开发基于差分隐私及其变体的严格隐私概念，并研究最优和启发式扰动机制，以实现具有最优效用的隐私保障；2)扩展保护轨迹或时间-位置对序列的隐私解决方案；3)考虑用户偏好、时空语义和隐私效用权衡，通过策略图开发可定制的位置和轨迹保护隐私解决方案。在基于位置的应用程序（如预测范围查询、k近邻查询以及地理空间众包）的背景下，研究了使用扰动位置的隐私-效用权衡。关于这个项目的更多信息可以在http://www.mathcs.emory.edu/aims/stprivacy上找到

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