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Occupant privacy perception, awareness, and preferences in smart office environments

Building management systems tout numerous benefits, such as energy efficiency and occupant comfort but rely on vast amounts of data from various sensors. Advancements in machine learning algorithms make it possible to extract personal information about occupants and their activities beyond the inten...

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Autores principales: Li, Beatrice, Tavakoli, Arash, Heydarian, Arsalan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10008538/
https://www.ncbi.nlm.nih.gov/pubmed/36906709
http://dx.doi.org/10.1038/s41598-023-30788-5
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author Li, Beatrice
Tavakoli, Arash
Heydarian, Arsalan
author_facet Li, Beatrice
Tavakoli, Arash
Heydarian, Arsalan
author_sort Li, Beatrice
collection PubMed
description Building management systems tout numerous benefits, such as energy efficiency and occupant comfort but rely on vast amounts of data from various sensors. Advancements in machine learning algorithms make it possible to extract personal information about occupants and their activities beyond the intended design of a non-intrusive sensor. However, occupants are not informed of data collection and possess different privacy preferences and thresholds for privacy loss. While privacy perceptions and preferences are most understood in smart homes, limited studies have evaluated these factors in smart office buildings, where there are more users and different privacy risks. To better understand occupants’ perceptions and privacy preferences, we conducted twenty-four semi-structured interviews between April 2022 and May 2022 on occupants of a smart office building. We found that data modality features and personal features contribute to people’s privacy preferences. The features of the collected modality define data modality features – spatial, security, and temporal context. In contrast, personal features consist of one’s awareness of data modality features and data inferences, definitions of privacy and security, and the available rewards and utility. Our proposed model of people’s privacy preferences in smart office buildings helps design more effective measures to improve people’s privacy.
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spelling pubmed-100085382023-03-13 Occupant privacy perception, awareness, and preferences in smart office environments Li, Beatrice Tavakoli, Arash Heydarian, Arsalan Sci Rep Article Building management systems tout numerous benefits, such as energy efficiency and occupant comfort but rely on vast amounts of data from various sensors. Advancements in machine learning algorithms make it possible to extract personal information about occupants and their activities beyond the intended design of a non-intrusive sensor. However, occupants are not informed of data collection and possess different privacy preferences and thresholds for privacy loss. While privacy perceptions and preferences are most understood in smart homes, limited studies have evaluated these factors in smart office buildings, where there are more users and different privacy risks. To better understand occupants’ perceptions and privacy preferences, we conducted twenty-four semi-structured interviews between April 2022 and May 2022 on occupants of a smart office building. We found that data modality features and personal features contribute to people’s privacy preferences. The features of the collected modality define data modality features – spatial, security, and temporal context. In contrast, personal features consist of one’s awareness of data modality features and data inferences, definitions of privacy and security, and the available rewards and utility. Our proposed model of people’s privacy preferences in smart office buildings helps design more effective measures to improve people’s privacy. Nature Publishing Group UK 2023-03-11 /pmc/articles/PMC10008538/ /pubmed/36906709 http://dx.doi.org/10.1038/s41598-023-30788-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Li, Beatrice
Tavakoli, Arash
Heydarian, Arsalan
Occupant privacy perception, awareness, and preferences in smart office environments
title Occupant privacy perception, awareness, and preferences in smart office environments
title_full Occupant privacy perception, awareness, and preferences in smart office environments
title_fullStr Occupant privacy perception, awareness, and preferences in smart office environments
title_full_unstemmed Occupant privacy perception, awareness, and preferences in smart office environments
title_short Occupant privacy perception, awareness, and preferences in smart office environments
title_sort occupant privacy perception, awareness, and preferences in smart office environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10008538/
https://www.ncbi.nlm.nih.gov/pubmed/36906709
http://dx.doi.org/10.1038/s41598-023-30788-5
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