| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2015: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Outline of Final Research Achievements |
We verified the feasibility of a proof-of-concept side-channel attack that identifies a route for a train trip by simply reading smart device sensors: an accelerometer, magnetometer, and gyroscope, which are commonly used by many apps without requiring any permissions. First, by applying a machine-learning technique to the data collected from sensors, we can detect the activity of a user, i.e., walking, in moving vehicle, or other. Next, we extract departure/arrival times of vehicles from the sequence of the detected human activities. Finally, by correlating the detected departure/arrival times of the vehicle with timetables/route maps collected from all the railway companies in the rider’s country, it identifies potential routes that can be used for a trip. We demonstrate that the strategy is feasible through field experiments. Building wrapper APIs that provide many useful functions, while hiding raw data, is a promising approach to thwart sensor-based side-channel attacks.
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