Entering text on a smartwatch is challenging due to the difficulty of tapping tiny keys. This study introduces a novel keyboard, Tap’nSwipe, to address the challenge. The keyboard features nine areas, each containing up to four characters. To enter a character, users swipe in a specific direction within the area containing the character, freeing them from precisely tapping on the target key. In addition, Tap’nSwipe leverages word predictions to enter words by allowing users to tap anywhere in the areas containing the target characters. The results of a user experiment show that Tap’nSwipe improves text entry accuracy and reduces error correction efforts when entering random character strings on a small smartwatch screen, with a marginal decrease in tying speed compared to QWERTY. Participants rated Tap’nSwipe higher than QWERTY in perceived character clarity, interaction precision, typing accuracy, typing efficiency, and ease of use. Participants also expressed strong interest in adopting Tap’nSwipe.

Assistive technology is extremely important for maintaining and improving the elderly’s quality of life. Biometrics-based mobile user authentication (MUA) methods have witnessed rapid development in recent years owing to their usability and security benefits. However, there is a lack of a comprehensive review of such methods for the elderly. The primary objective of this research is to analyze the literature on state-of-the-art biometrics-based MUA methods via the lens of elderly users’ accessibility needs. In addition, conducting an MUA user study with elderly participants faces significant challenges, and it remains unclear how the performance of the elderly compares with non-elderly users in biometrics-based MUA. To this end, this research summarizes method design principles for user studies involving elderly participants and reveals the performance of elderly users relative to non-elderly users in biometrics-based MUA. The article also identifies open research issues and provides suggestions for the design of effective and accessible biometrics-based MUA methods for the elderly.

Shoulder-surfing studies in the context of mobile user authentication have focused on evaluating the attackers' performance, yet have paid much less attention to their perception of the shoulder-surfing process. Whether and how the shoulder-surfing setting might affect the attackers' perception remains under-explored. This study aims to investigate the perception of shoulder surfers with two different password-based mobile user authentication methods and three different observation angles. Moreover, this work examines the relationship between the attackers' perception and performance in shoulder surfing and the possible moderating effect of the authentication method for the first time. Based on the data collected from an online experiment, our analysis results reveal the effects of authentication methods and observation angles on the attackers' perception in terms of cognitive workload, observation clarity, and repetitive learning advantage. In addition, the results also show that the relationship between the attackers' cognitive workload and performance in shoulder surfing varies with the mobile user authentication method. Our findings not only deepen the understanding of shoulder-surfing attacks from an attacker's perspective, but also facilitate developing countermeasures for shoulder-surfing attacks.

The pervasive use of mobile devices exposes users to an elevated risk of shoulder-surfing attacks. Despite the prior work on shoulder-surfing resistance of mobile user authentication methods, there is a lack of empirical studies on textual password authentication methods, particularly the hybrid passwords that integrate textual passwords with biometrics. To fill the literature gap, this research compares two hybrid password methods, touch-gesture- and keystroke-based passwords, with respect to their shoulder-surfing resistance performance. We select a touch-gesture-based password method that deploys multiple shoulder-surfing resistance strategies and a keystroke-based password method that leverages keystroke dynamics. To gain a holistic understanding of these password methods, we examine them under a variety of shoulder-surfing settings by varying interaction mode, observation angle, entry error, and observation effort. Going beyond effectiveness metrics, we also introduce efficiency metrics to assess shoulder-surfing resistance performance more comprehensively. We hypothesize and test the effects of shoulder-surfing settings by conducting both a longitudinal lab experiment and an online experiment with diversified participants. The results of both studies demonstrate the superior performance of the touch-gesture-based password method to the keystroke-based counterpart. The results also provide evidence for the effects of interaction mode, observation angle, and observation effort on shoulder-surfing resistance of hybrid passwords. Our findings offer suggestions for the design and strategies for strengthening the security of password authentication methods.

Domestic violence (DV) can lead to physical, psychological, and/or emotional consequences for its victims. Social media provides a new platform for DV victims to share their personal experiences and seek needed support. The anonymity of social media can potentially provide comfort and safety for victims to disclose their victimization experience. Despite a few efforts in detecting DV from social media, they have focused on differentiating DV-from non-DV-related content, or classifying DV-related content into a few general categories. By conducting an in-depth analysis of the content of DV self-disclosure in social media, this study characterizes DV in multiple aspects for the first time, including victim, perpetrator, relationship, and abuse. Moreover, it identifies the attributes to describe each aspect in detail. Furthermore, we use the social media data generated during the COVID-19 pandemic as a case study to understand the patterns of DV. The research findings of this study have implications for increasing the awareness of DV and designing support for DV victims.

Password-based mobile user authentication is vulnerable to shoulder-surfing. Despite the increasing research on user password entry behavior and mobile security, there is limited understanding of how an adversary identifies a password through shoulder-surfing during mobile authentication. This study empirically examines the behaviors and strategies of password identification through shoulder-surfing with multiple observation attempts and from different observation distances. The results of analyzing data collected from a user study reveal the strategies and dynamics of password identification behaviors. The findings have implications for enhancing users’ password security and improving the design of mobile authentication methods.

As it becomes commonplace to use mobile devices to store personal and sensitive data, mobile user authentication (MUA) methods have witnessed significant advancement to improve data and device security. On the other hand, traditional MUA methods such as password (or passcode) are still being widely deployed. Despite the growing body of knowledge on technical strengths and security vulnerabilities of various MUA methods, the perception of mobile users may be different, which can play a decisive role in MUA adoption. Additionally, user preferences for MUA methods may be subject to the influence of their demographic factors and device types. Furthermore, the pervasive use of mobile devices has generated many situations that create new usability and security needs of MUA methods such as support of one-handed and/or sight-free interaction. This study investigates user perception and situational needs of MUA methods using a survey questionnaire. The research findings can guide the design and selection of MUA methods.