arXiv (Cornell University) · 2015 · 95 citations · 25 references
ComputingComputer SystemsMachine LearningMobile ComputingMobile SystemsEnergy-efficient Mobile ComputingLocation AnalyticsLocation PrivacyMobile Human-computer InteractionMobile PrivacyMobile SensingEnergy-efficient Machine Learning On Mobile DevicesEnergy-efficient Mobile SensingMachine Learning-based Power EstimationMobile Device Energy ProfilingMobile Device Location DataMobile Location-aware SystemsPower ConsumptionPower Consumption Modeling
Modern mobile platforms like Android enable applications to read aggregate power usage on the phone. This information is considered harmless and reading it requires no user permission or notification. We show that by simply reading the phone's aggregate power consumption over a period of a few minutes an application can learn information about the user's location. Aggregate phone power consumption data is extremely noisy due to the multitude of components and applications that simultaneously consume power. Nevertheless, by using machine learning algorithms we are able to successfully infer the phone's location. We discuss several ways in which this privacy leak can be remedied.
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