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Tangible Privacy for Smart Voice Assistants: Bystanders' Perceptions of Physical Device Controls

Authors:

Adam J. LeeAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 6, Issue CSCW2

Article No.: 364, Pages 1 - 31

https://doi.org/10.1145/3555089

Published: 11 November 2022 Publication History

Abstract

Smart voice assistants such as Amazon Alexa and Google Home are becoming increasingly pervasive in our everyday environments. Despite their benefits, their miniaturized and embedded cameras and microphones raise important privacy concerns related to surveillance and eavesdropping. Recent work on the privacy concerns of people in the vicinity of these devices has highlighted the need for 'tangible privacy', where control and feedback mechanisms can provide a more assured sense of whether the camera or microphone is 'on' or 'off'. However, current designs of these devices lack adequate mechanisms to provide such assurances. To address this gap in the design of smart voice assistants, especially in the case of disabling microphones, we evaluate several designs that incorporate (or not) tangible control and feedback mechanisms. By comparing people's perceptions of risk, trust, reliability, usability, and control for these designs in a between-subjects online experiment (N=261), we find that devices with tangible built-in physical controls are perceived as more trustworthy and usable than those with non-tangible mechanisms. Our findings present an approach for tangible, assured privacy especially in the context of embedded microphones.

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Index Terms

Tangible Privacy for Smart Voice Assistants: Bystanders' Perceptions of Physical Device Controls
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing
2. Security and privacy

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Proceedings of the ACM on Human-Computer Interaction Volume 6, Issue CSCW2

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November 2022

8205 pages

EISSN:2573-0142

DOI:10.1145/3571154

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Published: 11 November 2022

Published in PACMHCI Volume 6, Issue CSCW2

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Cited By

Keppel JStrauss MGruenefeld USchneegass S(2024)Magic Mirror: Designing a Weight Change Visualization for Domestic UseProceedings of the ACM on Human-Computer Interaction10.1145/36981498:ISS(569-596)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3698149
Leschanowsky ARech SPopp BBäckström T(2024)Evaluating privacy, security, and trust perceptions in conversational AIComputers in Human Behavior10.1016/j.chb.2024.108344159:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.chb.2024.108344
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