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research-article

Finding the Stars in the Fireworks: Deep Understanding of Motion Sensor Fingerprint

Published: 01 October 2019 Publication History

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.

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  • (2024)Watch the Rhythm: Breaking Privacy with Accelerometer at the Extremely-Low Sampling Rate of 5HzProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3690370(1776-1790)Online publication date: 2-Dec-2024
  • (2023)Accuth$^+$+: Accelerometer-Based Anti-Spoofing Voice Authentication on Wrist-Worn WearablesIEEE Transactions on Mobile Computing10.1109/TMC.2023.331483723:5(5571-5588)Online publication date: 13-Sep-2023
  • (2022)AccuthProceedings of the 20th ACM Conference on Embedded Networked Sensor Systems10.1145/3560905.3568522(637-650)Online publication date: 6-Nov-2022
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Published In

cover image IEEE/ACM Transactions on Networking
IEEE/ACM Transactions on Networking  Volume 27, Issue 5
October 2019
407 pages

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IEEE Press

Publication History

Published: 01 October 2019
Published in TON Volume 27, Issue 5

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View all
  • (2024)Watch the Rhythm: Breaking Privacy with Accelerometer at the Extremely-Low Sampling Rate of 5HzProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3690370(1776-1790)Online publication date: 2-Dec-2024
  • (2023)Accuth$^+$+: Accelerometer-Based Anti-Spoofing Voice Authentication on Wrist-Worn WearablesIEEE Transactions on Mobile Computing10.1109/TMC.2023.331483723:5(5571-5588)Online publication date: 13-Sep-2023
  • (2022)AccuthProceedings of the 20th ACM Conference on Embedded Networked Sensor Systems10.1145/3560905.3568522(637-650)Online publication date: 6-Nov-2022
  • (2022)Electromagnetic Fingerprinting of Memory HeartbeatsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35502956:3(1-23)Online publication date: 7-Sep-2022
  • (2020)DeepRangeProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34321954:4(1-23)Online publication date: 18-Dec-2020

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