Abstract
Intelligent workplace systems that support well-being offer the potential to reduce stress and burnout, promote physical activity, or increase creativity and collaboration while at work. However, such systems rely on the collection of sensitive personal information that can pose significant privacy risks to users. In this chapter, we investigate how user perceptions of privacy vary with privacy interface designs and framing scenarios. In a user study with 60 participants, we present participants with four privacy interfaces based on different privacy frameworks and study how perceptions of comfort and control vary depending on the owner of the sensing technology and the user’s relationship with that owner. We find that participants express greater comfort and control with interfaces that foreground contextual information and provide relationship-based access control. Moreover, participants display lower feelings of comfort and control when the technology is deployed company-wide or by a manager with whom they have a negative relationship. Concerningly, we find that interfaces based on technical privacy metrics are poorly understood and have the potential to promote a false sense of security. Taken together, our findings have implications for the design of privacy interfaces and can inform future large-scale studies on privacy attitudes in the workplace.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adler, D. A., Tseng, E., Moon, K. C., Young, J. Q., Kane, J. M., Moss, E., Mohr, D. C., & Choudhury, T. (2022). Burnout and the quantified workplace: Tensions around personal sensing interventions for stress in resident physicians. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 1–48.
Barth, S., de Jong, M. D., Junger, M., Hartel, P. H., & Roppelt, J. C. (2019). Putting the privacy paradox to the test: Online privacy and security behaviors among users with technical knowledge, privacy awareness, and financial resources. Telematics and Informatics, 41, 55–69.
Conn, V. S., Hafdahl, A. R., Cooper, P. S., Brown, L. M., & Lusk, S. L. (2009). Meta-analysis of workplace physical activity interventions. American Journal of Preventive Medicine, 37(4), 330–339.
Corbyn, Z. (2022). ‘Bossware is coming for almost every worker’: The software you might not realize is watching you. https://www.theguardian.com/technology/2022/apr/27/remote-work-software-home-surveillance-computer-monitoring-pandemic
Costa, J., Guimbretière, F., Jung, M.F., Choudhury, T. (2019). Boostmeup: Improving cognitive performance in the moment by unobtrusively regulating emotions with a smartwatch. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(2), 1–23.
Dechand, S., Naiakshina, A., Danilova, A., & Smith, M. (2019). In encryption we don’t trust: The effect of end-to-end encryption to the masses on user perception. In 2019 IEEE European Symposium on Security and Privacy (EuroS&P) (pp. 401–415). IEEE.
Diel, S., Gutheil, N., Richter, F., & Buck, C. (2022). My data, my choice?! the difference between fitness and stress data monitoring on employees’ perception of privacy. In Proceedings of the 55th Hawaii International Conference on System Sciences.
Dwork, C. (2008). Differential privacy: A survey of results. In International conference on theory and applications of models of computation (pp. 1–19). Springer.
Fong, P. W. (2011). Relationship-based access control: protection model and policy language. In: Proceedings of the first ACM conference on Data and application security and privacy (pp. 191–202).
Harwell, D. (2019). Is your pregnancy app sharing your intimate data with your boss? https://www.washingtonpost.com/technology/2019/04/10/tracking-your-pregnancy-an- app-may-be-more-public-than-you-think/
Howe, E., Suh, J., Bin Morshed, M., McDuff, D., Rowan, K., Hernandez, J., Abdin, M. I., Ramos, G., Tran, T., & Czerwinski, M. P. (2022). Design of digital workplace stress-reduction intervention systems: Effects of intervention type and timing. In CHI Conference on Human Factors in Computing Systems (pp. 1–16).
Humbert, M., Trubert, B., & Huguenin, K. (2019). A survey on interdependent privacy. ACM Computing Surveys (CSUR), 52(6), 1–40.
Jun, E., McDuff, D., & Czerwinski, M. (2019). Circadian rhythms and physiological synchrony: Evidence of the impact of diversity on small group creativity. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–22.
Kang, R., Dabbish, L., Fruchter, N., & Kiesler, S. (2015). My data just goes Everywhere:” User mental models of the internet and implications for privacy and security. In Eleventh Symposium on Usable Privacy and Security (SOUPS 2015) (pp. 39–52).
Kantor, J., & Sundaram, A. (2022). The rise of the worker productivity score. https://www.nytimes.com/interactive/2022/08/14/business/worker-productivity-tracking.html
Kimani, E., Rowan, K., McDuff, D., Czerwinski, M., & Mark, G. (2019). A conversational agent in support of productivity and wellbeing at work. In: 2019 8th international conference on affective computing and intelligent interaction (ACII) (pp. 1–7). IEEE.
Mandryk, R. L., & Inkpen, K. M. (2004). Physiological indicators for the evaluation of co-located collaborative play. In: Proceedings of the 2004 ACM conference on Computer supported cooperative work (pp. 102–111).
Mark, G., Iqbal, S., & Czerwinski, M. (2017). How blocking distractions affects workplace focus and productivity. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (pp. 928–934).
Mendel, T., & Toch, E. (2017). Susceptibility to social influence of privacy behaviors: Peer versus authoritative sources. In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 581–593).
Nissenbaum, H. (2009). Privacy in Context. Stanford University Press.
Partnership on AI. (2022). Framework for promoting workforce well-being in the ai-integrated work-place.
Petronio, S. (2002). Boundaries of privacy: Dialectics of disclosure. Suny Press.
Sloat, S. (2022). When my employer provides my mental health app, how much data do they have access to? https://foundation.mozilla.org/en/blog/mental-health-awareness-2022-employer-access/
Sweeney, L. (2002). Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 571–588.
Tyagi, A., Squicciarini, A., Rajtmajer, S., & Griffin, C. (2016). An in-depth study of peer influence on collective decision making for multi-party access control. In: 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI) (pp. 305–314). IEEE.
Wu, J., & Zappala, D. (2018). When is a tree really a truck? exploring mental models of encryption. In: Fourteenth Symposium on Usable Privacy and Security (SOUPS 2018) (pp. 395–409).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
1.1 1. Framing Scenarios
1.1.1 1.1 Company-Oriented
You are an employee at a large tech company called SearchCo. The company has recently started a pilot program to improve employees’ general well-being by collecting data using work-issued devices like laptops and phones. To allow employees to manage the privacy of this data, the company has created an interface where employees can control what data the company is allowed to analyze.
1.1.2 1.2 Team-Oriented
You are an engineer on the News App team at a large tech company called SearchCo. Your team (the News App team) has recently started a pilot program to improve team members’ general well-being by collecting data using work-issued devices like laptops and phones. To allow team members to manage the privacy of this data, the News App team has created an interface where employees can control what data the team is allowed to analyze.
1.1.3 1.3 Manager-Oriented (Positive)
You are an engineer on a small eight-person team, and you have a very positive, strong relationship with your manager, Jamie. You’ve worked with Jamie for 2 years already, and you trust that they make great decisions for the team.
Jamie has recently started a pilot program to improve team members’ general well-being by collecting data using work-issued devices like laptops and phones. To allow team members to manage the privacy of this data, Jamie has created an interface where employees can control what data can be analyzed.
1.1.4 1.4 Manager-Oriented (Negative)
You are an engineer on a small eight-person team, and you have a very negative, strained relationship with your manager, Jamie. You’ve worked with Jamie for 2 years already, and you’ve witnessed them making questionable decisions for the team.
Jamie has recently started a pilot program to improve team members’ general well-being by collecting data using work-issued devices like laptops and phones. To allow team members to manage the privacy of this data, Jamie has created an interface where employees can control what data can be analyzed.
1.1.5 1.5 Teammate-Oriented (Positive)
You are an engineer on a small eight-person team, and you have a very positive, strong relationship with your manager and teammates. You’ve worked on your team for 2 years already, and you trust that your manager and teammates all make great decisions.
One of your teammates, Jamie, has recently started a pilot program to improve team members’ general well-being by collecting data using work-issued devices like laptops and phones. To allow team members to manage the privacy of this data, Jamie has created an interface where employees can control what data can be analyzed.
1.1.6 1.6 Teammate-Oriented (Negative)
You are an engineer on a small eight-person team, and although you have a very positive, strong relationship with your manager and most teammates, you have a very negative, strained relationship with one of your teammates, Jamie. You’ve worked with this teammate for 2 years already, and you’ve witnessed them making questionable decisions for the team.
Jamie has recently started a pilot program to improve team members’ general well-being by collecting data using work-issued devices like laptops and phones. To allow team members to manage the privacy of this data, Jamie has created an interface where employees can control what data can be analyzed.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Lam, M.S., Jörke, M., King, J., Haghighi, N., Landay, J.A. (2023). User Perceptions of Privacy Interfaces in the Workplace. In: Meinel, C., Leifer, L. (eds) Design Thinking Research. Understanding Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-36103-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-36103-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36102-9
Online ISBN: 978-3-031-36103-6
eBook Packages: Business and ManagementBusiness and Management (R0)