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• W2794047627 abstract "With the proliferation of mobile devices and various sensors (e.g., GPS, magnetometer, accelerometers, gyroscopes) equipped, richer services, e.g. location based services, are provided to users. A series of methods have been proposed to protect the users' privacy, especially the trajectory privacy. Hardware fingerprinting has been demonstrated to be a surprising and effective source for identifying/authenticating devices. In this work, we show that a few data samples collected from the motion sensors are enough to uniquely identify the source mobile device, i.e., the raw motion sensor data serves as a fingerprint of the mobile device. Specifically, we first analytically understand the fingerprinting capacity using features extracted from hardware data. To capture the essential device feature automatically, we design a multi-LSTM neural network to fingerprint mobile device sensor in real-life uses, instead of using handcrafted features by existing work. Using data collected over 6 months, for arbitrary user movements, our fingerprinting model achieves 93 % F -score given one second data, while the state-of-the-art work achieves 79% F-score. Given ten seconds randomly sampled data, our model can achieve 98.8% accuracy. We also propose a novel generative model to modify the original sensor data and yield anonymized data with little fingerprint information while retain good data utility." @default.
• W2794047627 created "2018-03-29" @default.
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• W2794047627 date "2018-04-01" @default.
• W2794047627 modified "2023-10-01" @default.
• W2794047627 title "Finding the Stars in the Fireworks: Deep Understanding of Motion Sensor Fingerprint" @default.
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• W2794047627 doi "https://doi.org/10.1109/infocom.2018.8486226" @default.
• W2794047627 hasPublicationYear "2018" @default.
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