Abstract
The privacy of users and information are becoming increasingly important with the growth and pervasive use of mobile devices such as wearables, mobile phones, drones, and Internet of Things (IoT) devices. Today many of these mobile devices are equipped with cameras which enable users to take pictures and record videos anytime they need to do so. In many such cases, bystanders’ privacy is not a concern, and as a result, audio and video of bystanders are often captured without their consent. We present results from a user study in which 21 participants were asked to use a wearable system called FacePET developed to enhance bystanders’ facial privacy by providing a way for bystanders to protect their own privacy rather than relying on external systems for protection. While past works in the literature focused on privacy perceptions of bystanders when photographed in public/shared spaces, there has not been research with a focus on user perceptions of bystander-based wearable devices to enhance privacy. Thus, in this work, we focus on user perceptions of the FacePET device and/or similar wearables to enhance bystanders’ facial privacy. In our study, we found that 16 participants would use FacePET or similar devices to enhance their facial privacy, and 17 participants agreed that if smart glasses had features to conceal users’ identities, it would allow them to become more popular.
Document Type
Article
Publication Date
10-10-2020
Digital Object Identifier (DOI)
https://doi.org/10.3390/iot1020013
Funding Information
This research was supported by the U.S Department of Defense and the U.S. National Science Foundation under grant award # 1560214, and by the U.S. National Science Foundation under grant award # 1950416.
Repository Citation
Perez, Alfredo J.; Zeadally, Sherali; Griffith, Scott; Matos Garcia, Luis Y.; and Mouloud, Jaouad A., "A User Study of a Wearable System to Enhance Bystanders’ Facial Privacy" (2020). Information Science Faculty Publications. 74.
https://uknowledge.uky.edu/slis_facpub/74
Included in
Computer Engineering Commons, Computer Sciences Commons, Library and Information Science Commons
Notes/Citation Information
Published in IoT, v. 1, issue 2.
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).