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From Electromyogram to Password: Exploring the Privacy Impact of Wearables in Augmented Reality

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Published:04 September 2017Publication History
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Abstract

With the increasing popularity of augmented reality (AR) services, providing seamless human-computer interactions in the AR setting has received notable attention in the industry. Gesture control devices have recently emerged to be the next great gadgets for AR due to their unique ability to enable computer interaction with day-to-day gestures. While these AR devices are bringing revolutions to our interaction with the cyber world, it is also important to consider potential privacy leakages from these always-on wearable devices. Specifically, the coarse access control on current AR systems could lead to possible abuse of sensor data.

Although the always-on gesture sensors are frequently quoted as a privacy concern, there has not been any study on information leakage of these devices. In this article, we present our study on side-channel information leakage of the most popular gesture control device, Myo. Using signals recorded from the electromyography (EMG) sensor and accelerometers on Myo, we can recover sensitive information such as passwords typed on a keyboard and PIN sequence entered through a touchscreen. EMG signal records subtle electric currents of muscle contractions. We design novel algorithms based on dynamic cumulative sum and wavelet transform to determine the exact time of finger movements. Furthermore, we adopt the Hudgins feature set in a support vector machine to classify recorded signal segments into individual fingers or numbers. We also apply coordinate transformation techniques to recover fine-grained spatial information with low-fidelity outputs from the sensor in keystroke recovery.

We evaluated the information leakage using data collected from a group of volunteers. Our results show that there is severe privacy leakage from these commodity wearable sensors. Our system recovers complex passwords constructed with lowercase letters, uppercase letters, numbers, and symbols with a mean success rate of 91%.

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            • Published in

              cover image ACM Transactions on Intelligent Systems and Technology
              ACM Transactions on Intelligent Systems and Technology  Volume 9, Issue 1
              Regular Papers and Special Issue: Data-driven Intelligence for Wireless Networking
              January 2018
              258 pages
              ISSN:2157-6904
              EISSN:2157-6912
              DOI:10.1145/3134224
              • Editor:
              • Yu Zheng
              Issue’s Table of Contents

              Copyright © 2017 ACM

              © 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 4 September 2017
              • Accepted: 1 April 2017
              • Revised: 1 February 2017
              • Received: 1 November 2016
              Published in tist Volume 9, Issue 1

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