Normative and Non-Social Beliefs about Sensor Data: Implications for Collective Privacy Management

Authors: 

Emilee Rader, Michigan State University

Abstract: 

Sensors embedded in wearable and smart home devices collect data that can be used to infer sensitive, private details about people's lives. Privacy norms have been proposed as a foundation upon which people might coordinate to set and enforce preferences for acceptable or unacceptable data practices. Through a qualitative study, this research explored whether normative beliefs influenced participants' reactions to plausible but unexpected inferences that could be made from sensor data collected by everyday wearable and smart home devices. Some reactions were grounded in normative beliefs involving existing disclosure taboos, while others stigmatized the choice to limit one's use of technologies to preserve one's privacy. The visible nature of others' technology use contradicts individual concern about sensor data privacy, which may lead to an incorrect assumption that privacy is not important to other people. Findings suggest that this is a barrier to collective privacy management, and that awareness interventions focused on information about the beliefs of other users may be helpful for collective action related to data privacy.

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BibTeX
@inproceedings {281230,
author = {Emilee Rader},
title = {Normative and {Non-Social} Beliefs about Sensor Data: Implications for Collective Privacy Management},
booktitle = {Eighteenth Symposium on Usable Privacy and Security (SOUPS 2022)},
year = {2022},
isbn = {978-1-939133-30-4},
address = {Boston, MA},
pages = {653--670},
url = {https://www.usenix.org/conference/soups2022/presentation/rader},
publisher = {USENIX Association},
month = aug,
}