Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing
Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing
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DOI：
10.2478/popets-2019-0020
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发表时间：
2018-08
影响因子：
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通讯作者：
Sashank Narain;G. Noubir
中科院分区：
文献类型：
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作者：
Sashank Narain;G. Noubir
摘要我们提出了一个名为MATRIX的系统的设计、实现和评估，该系统旨在保护移动终端用户的隐私免受位置推断和传感器侧信道攻击。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）通过移动的应用程序访问。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。这些服务被设计为可扩展的，并且对用户来说很容易，隐藏了所有潜在的复杂性。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。这些移动性模式是通过使用随机线性规划对用户调度进行建模/求解以及使用二次规划对用户驾驶行为进行建模/求解来生成的。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的移动轨迹区分开来。
Abstract 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. These mobility 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.