
‘Over Time Everyone’s Gonna Be Open To It’: User Attitudes Towards Security and Privacy in Continuous Authentication for Smart Homes
Author(s) -
Heting Wang,
Yu-Peng Chen,
Dinank Bista,
Rodrigo Calvo,
Niriksha Regmi,
Xuanpu Zhang,
Tempestt Neal,
Jaime Ruiz,
Lisa Anthony
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3596512
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Continuous authentication (CA), a user authentication approach that continuously verifies a person’s identity without requiring explicit input, is increasingly being deployed in smart homes to maintain security posture throughout user sessions. However, prior research has overlooked user attitudes toward the increased data collection and surveillance associated with CA in smart homes. To bridge this gap, we conducted a focus group study with 33 participants, using a design probe video to simulate various CA implementation scenarios in smart homes. We explored participants’ current authentication methods (e.g., passwords and physiological biometrics) and examined their perceptions of CA. Through affinity diagramming, we found that participants perceive smart-home CA as presenting privacy and security challenges yet possessing great potential for enhanced usability. Participants also envisioned CA systems that offer more granular permission controls over personal data. Our findings indicate the contextual dependencies in balancing usability with privacy and security concerns. Our contributions include a comprehensive empirical dataset featuring the design probe video, participant transcripts, and a conceptual model of users’ nuanced understanding. We provide design recommendations for smart-home CA systems, emphasizing transparency as a crucial factor in building user trust and improving adoption rates.
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