TY - GEN
T1 - Inferring smartphone users’ handwritten patterns by using motion sensors
AU - Lee, Wei Han
AU - Ortiz, Jorge
AU - Ko, Bongjun
AU - Lee, Ruby
N1 - Funding Information:
This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
Publisher Copyright:
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Mobile devices including smartphones and wearable devices are increasingly gaining popularity as platforms for collecting and sharing sensor data, such as the accelerometer, gyroscope, and rotation sensor. These sensors are used to improve the convenience of smartphone users, e.g., supporting the mobile UI motion-based commands. Although these motion sensors do not require users’ permissions, they still bring potential risks of leaking users’ private information reflected by the changes of sensor readings. In this paper, we investigate the feasibility of inferring a user’s handwritten pattern on a smartphone touchscreen by using the embedded motion sensors. Specifically, our inference attack is composed of two key steps where we 1) first exploit the dynamic time warping (DTW) technique to differentiate any pair of time-series sensor recordings corresponding to different handwritten patterns; and 2) develop a novel sensor fusion mechanism to integrate information contained in multiple motion sensors by exploiting the majority voting strategy. Through extensive experiments using real-world data sets, we demonstrate the effectiveness of our proposed attack which can achieve 91.4% accuracy for inferring smartphone users’ handwritten patterns.
AB - Mobile devices including smartphones and wearable devices are increasingly gaining popularity as platforms for collecting and sharing sensor data, such as the accelerometer, gyroscope, and rotation sensor. These sensors are used to improve the convenience of smartphone users, e.g., supporting the mobile UI motion-based commands. Although these motion sensors do not require users’ permissions, they still bring potential risks of leaking users’ private information reflected by the changes of sensor readings. In this paper, we investigate the feasibility of inferring a user’s handwritten pattern on a smartphone touchscreen by using the embedded motion sensors. Specifically, our inference attack is composed of two key steps where we 1) first exploit the dynamic time warping (DTW) technique to differentiate any pair of time-series sensor recordings corresponding to different handwritten patterns; and 2) develop a novel sensor fusion mechanism to integrate information contained in multiple motion sensors by exploiting the majority voting strategy. Through extensive experiments using real-world data sets, we demonstrate the effectiveness of our proposed attack which can achieve 91.4% accuracy for inferring smartphone users’ handwritten patterns.
KW - Dynamic Timing Warping
KW - Handwritten Pattern
KW - Majority Voting
KW - Smartphone Sensors
UR - http://www.scopus.com/inward/record.url?scp=85052015233&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052015233&partnerID=8YFLogxK
U2 - 10.5220/0006650301390148
DO - 10.5220/0006650301390148
M3 - Conference contribution
AN - SCOPUS:85052015233
T3 - ICISSP 2018 - Proceedings of the 4th International Conference on Information Systems Security and Privacy
SP - 139
EP - 148
BT - ICISSP 2018 - Proceedings of the 4th International Conference on Information Systems Security and Privacy
A2 - Mori, Paolo
A2 - Furnell, Steven
A2 - Camp, Olivier
PB - SciTePress
T2 - 4th International Conference on Information Systems Security and Privacy, ICISSP 2018
Y2 - 22 January 2018 through 24 January 2018
ER -