使用动态应用沙箱缓解移动设备上的位置隐私攻击
Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing
摘要：
我们提出了一个系统，称为矩阵，开发保护隐私的移动终端用户从位置推断和传感器侧信道攻击的设计，实施和评估。MATRIX为用户提供了对位置和传感器的控制和可见性（例如，加速度计和陀螺仪）通过移动应用程序访问。它实现了一个PrivoScope服务，可以审计设备上应用程序的所有位置和传感器访问，并生成实时通知和图形以可视化这些访问;以及一个合成位置服务，使用户能够向他们认为有用但不信任其私人信息的应用程序提供模糊或合成的位置轨迹或传感器跟踪。这些服务的设计目的是可扩展且易于用户使用，从而向他们隐藏所有潜在的复杂性。MATRIX还实现了一个位置提供程序组件，该组件通过使用来自Google Maps Directions API的历史数据整合交通信息，并使用来自用户驾驶实验的统计信息整合加速度，为用户生成真实的隐私保护合成身份和轨迹。通过使用随机线性规划建模/求解用户调度和使用二次规划建模/求解用户驾驶行为来生成随机交通模式。我们使用用户研究、流行的位置驱动应用程序和机器学习技术对MATRIX进行了广泛的评估，并证明它可移植到全球大多数Android设备上，可靠，开销低，生成的合成轨迹难以与对手的真实移动轨迹区分开来。
英文摘要：
We present the design, implementation and evaluation of a system, called MATRIX, developed to protect the privacy of mobile device users from location inference and sensor side-channel attacks. MATRIX gives users control and visibility over location and sensor (e.g., Accelerometers and Gyroscopes) accesses by mobile apps. It implements a PrivoScope service that audits all location and sensor accesses by apps on the device and generates real-time notifications and graphs for visualizing these accesses; and a Synthetic Location service to enable users to provide obfuscated or synthetic location trajectories or sensor traces to apps they find useful, but do not trust with their private information. The services are designed to be extensible and easy for users, hiding all of the underlying complexity from them. MATRIX also implements a Location Provider component that generates realistic privacy-preserving synthetic identities and trajectories for users by incorporating traffic information using historical data from Google Maps Directions API, and accelerations using statistical information from user driving experiments. The random traffic patterns are generated by modeling/solving user schedule using a randomized linear program and modeling/solving for user driving behavior using a quadratic program. We extensively evaluated MATRIX using user studies, popular location-driven apps and machine learning techniques, and demonstrate that it is portable to most Android devices globally, is reliable, has low-overhead, and generates synthetic trajectories that are difficult to differentiate from real mobility trajectories by an adversary.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
