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1. Continuous User Authentication: A Vital Component of Mobile Security

   Johnson, Sydney; Muhammad, Jean (March 2025, The 2025 ADMI Symposium.)

   As mobile devices become increasingly integral to daily life, the need for robust security measures has intensified. Continuous user authentication (CUA) is an emerging paradigm designed to enhance security by verifying user identity throughout device usage, rather than solely at login. This study aims to explore user perceptions, experiences, and preferences concerning CUA methods, such as biometric scans (e.g., fingerprints, facial recognition) and behavioral analytics (e.g., typing patterns, swipe gestures). We will investigate the importance users place on continuous authentication for safeguarding personal data, as well as the usability challenges they encounter. Specifically, we will delve into how users perceive the reliability and accuracy of biometric and behavioral authentication methods, considering factors such as the perceived invasiveness of biometric scans and concerns about data privacy. Additionally, we will examine how perceptions and preferences for CUA vary across different age groups, as younger generations may be more accustomed to biometric authentication and less concerned about privacy implications, while older generations may have different preferences and concerns. The findings of this study will provide insights into user trust, privacy concerns, and the overall effectiveness of CUA in improving mobile security. By understanding user attitudes, this research seeks to inform the development of more intuitive and secure authentication solutions that align with user needs and expectations across various demographics. 

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2. Privacy attitudes and data valuation among fitness tracker users

   Vitak, J.; Liao, Y.; Kumar, P.; Zimmer, M.; Kritikos, K. (March 2018, Lecture notes in computer science)

   Fitness trackers are an increasingly popular tool for tracking one’s health and physical activity. While research has evaluated the potential benefits of these devices for health and well-being, few studies have empirically evaluated users’ behaviors when sharing personal fitness information (PFI) and the privacy concerns that stem from the collection, aggregation, and sharing of PFI. In this study, we present findings from a survey of Fitbit and Jawbone users (N=361) to understand how concerns about privacy in general and user- generated data in particular affect users’ mental models of PFI privacy, tracking, and sharing. Findings highlight the complex relationship between users’ demographics, sharing behaviors, privacy concerns, and internet skills with how valuable and sensitive they rate their PFI. We conclude with a discussion of opportunities to increase user awareness of privacy and PFI. 

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3. Privacy attitudes and data valuation among fitness tracker users

   https://doi.org/10.1007/978-3-319-78105-1_27  

   Vitak, J.; Liao, Y.; Kumar, P.; Zimmer, M.; Kritikos, K. (January 2018, Lecture notes in computer science)

   Fitness trackers are an increasingly popular tool for tracking one’s health and physical activity. While research has evaluated the potential benefits of these devices for health and well-being, few studies have empirically evaluated users’ behaviors when sharing personal fitness information (PFI) and the privacy concerns that stem from the collection, aggregation, and sharing of PFI. In this study, we present findings from a survey of Fitbit and Jawbone users (N=361) to understand how concerns about privacy in general and user- generated data in particular affect users’ mental models of PFI privacy, tracking, and sharing. Findings highlight the complex relationship between users’ demographics, sharing behaviors, privacy concerns, and internet skills with how valuable and sensitive they rate their PFI. We conclude with a discussion of opportunities to increase user awareness of privacy and PFI. 

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4. Understanding the role of privacy and trust in intelligent personal assistant adoption

   https://doi.org/10.1007/978-3-030-15742-5_9  

   Liao, Y.; Vitak, J.; Kumar, P.; Zimmer, M.; Kritikos, K. (March 2019, Proceedings of the 13th Annual iConference, Lecture Notes in Computer Science)

   Voice-controlled intelligent personal assistants (IPAs) have seen tremendous growth in recent years on smartphones and as standalone devices in people’s homes. While research has examined the potential benefits and drawbacks of these devices for IPA users, few studies have empirically evaluated the role of privacy and trust in individual decision to adopt IPAs. In this study, we present findings from a survey of IPA users and non-users (N=1160) to understand (1) the motivations and barriers to adopting IPAs and (2) how concerns about data privacy and trust in company compliance with social contract related to IPA data affect acceptance and use of IPAs. We discuss our findings in light of social contract theory and frameworks of technology acceptance. 

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5. A STATISTICAL OVERVIEW ON DATA PRIVACY

   Liu, Fang (August 2020, Notre Dame journal of law ethics public policy)

   The eruption of big data with the increasing collection and processing of vast volumes and variety of data have led to breakthrough discoveries and innovation in science, engineering, medicine, commerce, criminal justice, and national security that would not have been possible in the past. While there are many benefits to the collection and usage of big data, there are also growing concerns among the general public on what personal information is collected and how it is used. In addition to legal policies and regulations, technological tools and statistical strategies also exist to promote and safeguard individual privacy, while releasing and sharing useful population-level information. In this overview, I introduce some of these approaches, as well as the existing challenges and opportunities in statistical data privacy research and applications to better meet the practical needs of privacy protection and information sharing. 

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