TY - JOUR
T1 - Tracking the human mobility using mobile device sensors
AU - Watanabe, Takuya
AU - Akiyama, Mitsuaki
AU - Mori, Tatsuya
N1 - Publisher Copyright:
Copyright © 2017 The Institute of Electronics, Information and Communication Engineers.
PY - 2017/8
Y1 - 2017/8
N2 - We developed a novel, proof-of-concept side-channel attack framework called RouteDetector, which identifies a route for a train trip by simply reading smart device sensors: an accelerometer, magnetometer, and gyroscope. All these sensors are commonly used by many apps without requiring any permissions. The key technical components of RouteDetector can be summarized as follows. First, by applying a machine-learning technique to the data collected from sensors, RouteDetector detects the activity of a user, i.e., "walking," "in moving vehicle," or "other." Next, it extracts 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 and extensive simulation experiments using timetables and route maps for 9,090 railway stations of 172 railway companies.
AB - We developed a novel, proof-of-concept side-channel attack framework called RouteDetector, which identifies a route for a train trip by simply reading smart device sensors: an accelerometer, magnetometer, and gyroscope. All these sensors are commonly used by many apps without requiring any permissions. The key technical components of RouteDetector can be summarized as follows. First, by applying a machine-learning technique to the data collected from sensors, RouteDetector detects the activity of a user, i.e., "walking," "in moving vehicle," or "other." Next, it extracts 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 and extensive simulation experiments using timetables and route maps for 9,090 railway stations of 172 railway companies.
KW - Location identification
KW - Mobile security
KW - Side-channel attack
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U2 - 10.1587/transinf.2016ICP0022
DO - 10.1587/transinf.2016ICP0022
M3 - Article
AN - SCOPUS:85026500387
VL - E100D
SP - 1680
EP - 1690
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
SN - 0916-8532
IS - 8
ER -