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We present the design, implementation and evaluation of a system, calledMATRIX, developed to protect the privacy of mobile device users from locationinference and sensor side-channel attacks. MATRIX gives users control andvisibility over location and sensor (e.g., Accelerometers and Gyroscopes)accesses by mobile apps. It implements a PrivoScope service that audits alllocation and sensor accesses by apps on the device and generates real-timenotifications and graphs for visualizing these accesses; and a SyntheticLocation service to enable users to provide obfuscated or synthetic locationtrajectories or sensor traces to apps they find useful, but do not trust withtheir private information. The services are designed to be extensible and easyfor users, hiding all of the underlying complexity from them. MATRIX alsoimplements a Location Provider component that generates realisticprivacy-preserving synthetic identities and trajectories for users byincorporating traffic information using historical data from Google MapsDirections API, and accelerations using statistical information from userdriving experiments. The random traffic patterns are generated bymodeling/solving user schedule using a randomized linear program andmodeling/solving for user driving behavior using a quadratic program. Weextensively evaluated MATRIX using user studies, popular location-driven appsand machine learning techniques, and demonstrate that it is portable to mostAndroid devices globally, is reliable, has low-overhead, and generatessynthetic trajectories that are difficult to differentiate from real mobilitytrajectories by an adversary.

Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing