Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
110 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Exploring Smart Commercial Building Occupants' Perceptions and Notification Preferences of Internet of Things Data Collection in the United States (2303.04955v3)

Published 9 Mar 2023 in cs.CR, cs.CY, and cs.HC

Abstract: Data collection through the Internet of Things (IoT) devices, or smart devices, in commercial buildings enables possibilities for increased convenience and energy efficiency. However, such benefits face a large perceptual challenge when being implemented in practice, due to the different ways occupants working in the buildings understand and trust in the data collection. The semi-public, pervasive, and multi-modal nature of data collection in smart buildings points to the need to study occupants' understanding of data collection and notification preferences. We conduct an online study with 492 participants in the US who report working in smart commercial buildings regarding: 1) awareness and perception of data collection in smart commercial buildings, 2) privacy notification preferences, and 3) potential factors for privacy notification preferences. We find that around half of the participants are not fully aware of the data collection and use practices of IoT even though they notice the presence of IoT devices and sensors. We also discover many misunderstandings around different data practices. The majority of participants want to be notified of data practices in smart buildings, and they prefer push notifications to passive ones such as websites or physical signs. Surprisingly, mobile app notification, despite being a popular channel for smart homes, is the least preferred method for smart commercial buildings.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (49)
  1. Computer Networks, 192, June 2021.
  2. Peek-a-boo: I see your smart home activities, even encrypted! In Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks, WiSec ’20, page 207–218, New York, NY, USA, 2020. Association for Computing Machinery.
  3. Healthy buildings: How indoor spaces drive performance and productivity. Harvard University Press, 2020.
  4. Discovering smart home internet of things privacy norms using contextual integrity. 2(2), jul 2018.
  5. Real-time analysis of privacy-(un)aware iot applications. Proceedings on Privacy Enhancing Technologies, 2021(1):145–166, 2021.
  6. “what if?” predicting individual users’ smart home privacy preferences and their changes. Proceedings on Privacy Enhancing Technologies, 2019:211 – 231, 2019.
  7. A location privacy analysis of bluetooth mesh. Journal of Information Security and Applications, 54:102563, 2020.
  8. Privacy issues in smart buildings by examples in smart metering. 2019.
  9. The role of privacy fatigue in online privacy behavior. Computers in Human Behavior, 81:42–51, 2018.
  10. CookieYes. Gdpr cookie consent banner examples, November 2019.
  11. Council of European Union. General data protection regulation. https://gdpr-infor.eu, 2016.
  12. Lorrie Faith Cranor. Necessary but not sufficient: Standardized mechanisms for privacy notice and choice. J. on Telecomm. & High Tech. L., 10:273, 2012.
  13. Augmented reality’s potential for identifying and mitigating home privacy leaks. arXiv preprint arXiv:2301.11998, 2023.
  14. Personalized privacy assistants for the internet of things: Providing users with notice and choice. IEEE Pervasive Computing, 17(3):35–46, 2018.
  15. The visual microphone: Passive recovery of sound from video. 2014.
  16. Bolder is better: Raising user awareness through salient and concise privacy notices. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–12, 2021.
  17. Ask the experts: What should be on an iot privacy and security label? In 2020 IEEE Symposium on Security and Privacy (SP), pages 447–464. IEEE, 2020.
  18. The influence of friends and experts on privacy decision making in iot scenarios. Proc. ACM Hum.-Comput. Interact., 2(CSCW), November 2018.
  19. A design space for privacy choices: Towards meaningful privacy control in the internet of things. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–16, 2021.
  20. Light, entrainment and alertness: A case study in offices. Lighting Research & Technology, 52(6):736–750, 2020.
  21. Michelle Goddard. The eu general data protection regulation (gdpr): European regulation that has a global impact. International Journal of Market Research, 59(6):703–705, 2017.
  22. Eric Goldman. An introduction to the california consumer privacy act (ccpa). Santa Clara Univ. Legal Studies Research Paper, 2020.
  23. User privacy concerns in commercial smart buildings. Journal of Computer Security, (Preprint):1–33, 2022.
  24. Iot inspector: Crowdsourcing labeled network traffic from smart home devices at scale. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 4(2), jun 2020.
  25. An unsupervised hierarchical clustering based heuristic algorithm for facilitated training of electricity consumption disaggregation systems. Advanced Engineering Informatics, 28(4):311–326, 2014.
  26. Jennifer Huddleston. The price of privacy: The impact of strict data regulations on innovation and more. https://www.americanactionforum.org/insight/the-price-of-privacy-the-impact-of-strict-data-regulations-on-innovation-and-more/, 2021.
  27. Exploring the needs of users for supporting privacy-protective behaviors in smart homes. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1–19, 2022.
  28. Privacy and the internet of things. Modern Socio-Technical Perspectives on Privacy, page 233, 2022.
  29. The smart thermostat: using occupancy sensors to save energy in homes. In Proceedings of the 8th ACM conference on embedded networked sensor systems, pages 211–224, 2010.
  30. Privacy expectations and preferences in an iot world. In Thirteenth Symposium on Usable Privacy and Security (SOUPS 2017), pages 399–412, Santa Clara, CA, July 2017. USENIX Association.
  31. Overwhelming, important, irrelevant: Terms of service and privacy policy reading among older adults. In Proceedings of the 10th International Conference on Social Media and Society, pages 166–173, 2019.
  32. Office of the California Attorney General. California consumer privacy act (ccpa): First modified regulations. https://oag.ca.gov/sites/all/files/agweb/pdfs/privacy/ccpa-text-of-mod-clean-020720.pdf, 2020.
  33. Towards privacy-aware smart buildings: Capturing, communicating, and enforcing privacy policies and preferences. In 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), pages 193–198, 2017.
  34. Methods for Testing and Evaluating Survey Questions, chapter 1, pages 1–22. John Wiley & Sons, Ltd, 2004.
  35. Information exposure from consumer iot devices: A multidimensional, network-informed measurement approach. In Proceedings of the Internet Measurement Conference, IMC ’19, page 267–279, New York, NY, USA, 2019. Association for Computing Machinery.
  36. Survey on enterprise internet-of-things systems (e-iot): A security perspective, 2021.
  37. Poisonivy: (in)secure practices of enterprise iot systems in smart buildings. Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, 2020.
  38. Michael Rubinstein et al. Analysis and visualization of temporal variations in video. PhD thesis, Massachusetts Institute of Technology, 2014.
  39. A design space for effective privacy notices. In Eleventh Symposium On Usable Privacy and Security ({normal-{\{{SOUPS}normal-}\}} 2015), pages 1–17, 2015.
  40. Kratos: multi-user multi-device-aware access control system for the smart home. Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks, 2020.
  41. “it would probably turn into a social faux-pas”: Users’ and bystanders’ preferences of privacy awareness mechanisms in smart homes. In CHI Conference on Human Factors in Computing Systems, pages 1–13, 2022.
  42. Are those steps worth your privacy? fitness-tracker users’ perceptions of privacy and utility. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 5(4), dec 2022.
  43. Exploring non-urgent smart home notifications using a smart plant system. In 19th International Conference on Mobile and Ubiquitous Multimedia, MUM ’20, page 47–58, New York, NY, USA, 2020. Association for Computing Machinery.
  44. Exploring occupant behavior in buildings. Wagner, A., O’Brien, W., Dong, B., Eds, 2018.
  45. The smart building privacy challenge. In Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pages 238–239, 2021.
  46. Defending my castle: A co-design study of privacy mechanisms for smart homes. In Proceedings of the 2019 chi conference on human factors in computing systems, pages 1–12, 2019.
  47. Privacy perceptions and designs of bystanders in smart homes. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW):1–24, 2019.
  48. “did you know this camera tracks your mood?”: Understanding privacy expectations and preferences in the age of video analytics. Proceedings on Privacy Enhancing Technologies, 2021(2):282–304, 2021.
  49. Facial recognition: Understanding privacy concerns and attitudes across increasingly diverse deployment scenarios. In Seventeenth Symposium on Usable Privacy and Security (SOUPS 2021), pages 243–262, 2021.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (7)
  1. Tu Le (7 papers)
  2. Alan Wang (10 papers)
  3. Yaxing Yao (16 papers)
  4. Yuanyuan Feng (23 papers)
  5. Arsalan Heydarian (22 papers)
  6. Norman Sadeh (19 papers)
  7. Yuan Tian (183 papers)
Citations (6)

Summary

We haven't generated a summary for this paper yet.

Youtube Logo Streamline Icon: https://streamlinehq.com