People often store private and sensitive data on their mobile devices, and the security of these devices is essential. This project advances and develops a new process for verifying a user's legitimate right to access a mobile device. Existing research has not made this process very usable for many people who lack dexterity or the use of both hands. This research aims to design and develop a method for one-handed authentication on a touch-screen mobile handheld device. The objective is to improve both security and usability of authentication. The proposed methods also will detect unauthorized access to a mobile device in a continuous manner, even if the password is stolen. The interdisciplinary nature of this work will promote teaching, training, and education in mobile security and privacy, human-computer interaction, mobile accessibility, machine learning, and behavioral science. The researchers will actively engage students at both graduate and undergraduate levels in their research activities, and make a strong effort to engage women and underrepresented minorities.

The project will support one-handed mobile authentication on a touch-screen mobile handheld device by inducing thumb biometrics and by enabling one-handed text entry based on thumb strokes. This project will advance authentication research and practice by: (1) laying the groundwork for one-handed authentication in support of both point-of-entry and implicit continuous authentication; (2) introducing a new venue for improving the security of one-handed authentication by inducing and fusing thumb biometrics from user interactions with a touch-screen mobile device; (3) creating new design principles for improving the usability of mobile authentication; and (4) addressing accessibility challenges for users with situational or visual impairments via the support of keypress-less text entry on a mobile touch screen. This project will lend itself to a new solution that can address the common security-usability tradeoff of mobile authentication methods.

Agency
National Science Foundation (NSF)
Institute
Division of Computer and Network Systems (CNS)
Type
Standard Grant (Standard)
Application #
1917537
Program Officer
Balakrishnan Prabhakaran
Project Start
Project End
Budget Start
2018-08-15
Budget End
2021-07-31
Support Year
Fiscal Year
2019
Total Cost
$626,030
Indirect Cost
Name
University of North Carolina at Charlotte
Department
Type
DUNS #
City
Charlotte
State
NC
Country
United States
Zip Code
28223