Understanding the threats caused by correlating human mobility and smart device sensors
| Project/Area Number |
15K12038
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Information security
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| Research Institution | Waseda University |
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Principal Investigator |
Mori Tatsuya 早稲田大学, 理工学術院, 准教授 (60708551)
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| Research Collaborator |
WATANABE Takuya
AKIYAMA Mitsuaki
YAGI Takeshi
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| Project Period (FY) |
2015-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| 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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| Keywords | センサー / セキュリティ / プライバシー / 機械学習 / スマートフォン / 攻撃 / 位置情報 / 推定 / サイドチャネル |
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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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Report
(3 results)
Research Products
(5 results)
