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Every Move You Make: Exploring Practical Issues in Smartphone Motion Sensor Fingerprinting and Countermeasures

Every Move You Make: Exploring Practical Issues in Smartphone Motion Sensor Fingerprinting and... AbstractThe ability to track users’ activities across different websites and visits is a key tool in advertising and surveillance. The HTML5 DeviceMotion interface creates a new opportunity for such tracking via fingerprinting of smartphone motion sensors. We study the feasibility of carrying out such fingerprinting under real-world constraints and on a large scale. In particular, we collect measurements from several hundred users under realistic scenarios and show that the state-of-the-art techniques provide very low accuracy in these settings. We then improve fingerprinting accuracy by changing the classifier as well as incorporating auxiliary information. We also show how to perform fingerprinting in an open-world scenario where one must distinguish between known and previously unseen users.We next consider the problem of developing fingerprinting countermeasures; we evaluate the usability of a previously proposed obfuscation technique and a newly developed quantization technique via a large-scale user study. We find that both techniques are able to drastically reduce fingerprinting accuracy without significantly impacting the utility of the sensors in web applications. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings on Privacy Enhancing Technologies de Gruyter

Every Move You Make: Exploring Practical Issues in Smartphone Motion Sensor Fingerprinting and Countermeasures

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Publisher
de Gruyter
Copyright
© 2018 Anupam Das et al., published by De Gruyter Open
ISSN
2299-0984
eISSN
2299-0984
DOI
10.1515/popets-2018-0005
Publisher site
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Abstract

AbstractThe ability to track users’ activities across different websites and visits is a key tool in advertising and surveillance. The HTML5 DeviceMotion interface creates a new opportunity for such tracking via fingerprinting of smartphone motion sensors. We study the feasibility of carrying out such fingerprinting under real-world constraints and on a large scale. In particular, we collect measurements from several hundred users under realistic scenarios and show that the state-of-the-art techniques provide very low accuracy in these settings. We then improve fingerprinting accuracy by changing the classifier as well as incorporating auxiliary information. We also show how to perform fingerprinting in an open-world scenario where one must distinguish between known and previously unseen users.We next consider the problem of developing fingerprinting countermeasures; we evaluate the usability of a previously proposed obfuscation technique and a newly developed quantization technique via a large-scale user study. We find that both techniques are able to drastically reduce fingerprinting accuracy without significantly impacting the utility of the sensors in web applications.

Journal

Proceedings on Privacy Enhancing Technologies – de Gruyter

Published: Jan 1, 2018

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    Brown
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