skip to main content
10.1145/3411764.3445067acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

PriView– Exploring Visualisations to Support Users’ Privacy Awareness

Published:07 May 2021Publication History

ABSTRACT

We present PriView, a concept that allows privacy-invasive devices in the users’ vicinity to be visualised. PriView is motivated by an ever-increasing number of sensors in our environments tracking potentially sensitive data (e.g., audio and video). At the same time, users are oftentimes unaware of this, which violates their privacy. Knowledge about potential recording would enable users to avoid accessing such areas or not to disclose certain information. We built two prototypes: a) a mobile application capable of detecting smart devices in the environment using a thermal camera, and b) VR mockups of six scenarios where PriView might be useful (e.g., a rental apartment). In both, we included several types of visualisation. Results of our lab study (N=24) indicate that users prefer simple, permanent indicators while wishing for detailed visualisations on demand. Our exploration is meant to support future designs of privacy visualisations for varying smart environments.

Skip Supplemental Material Section

Supplemental Material

3411764.3445067_videofigure.mp4

mp4

50.3 MB

References

  1. Yomna Abdelrahman, Paweł W. Woundefinedniak, Pascal Knierim, Dominik Weber, Ken Pfeuffer, Niels Henze, Albrecht Schmidt, and Florian Alt. 2019. Exploring the Domestication of Thermal Imaging. In Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia (Pisa, Italy) (MUM ’19). Association for Computing Machinery, New York, NY, USA, Article 9, 7 pages. https://doi.org/10.1145/3365610.3365648Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Anne Adams. 2000. Multimedia Information Changes the Whole Privacy Ballgame. In Proceedings of the Tenth Conference on Computers, Freedom and Privacy: Challenging the Assumptions (Toronto, Ontario, Canada) (CFP ’00). Association for Computing Machinery, New York, NY, USA, 25–32. https://doi.org/10.1145/332186.332199Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Debjanee Barua, Judy Kay, and Cécile Paris. 2013. Viewing and Controlling Personal Sensor Data: What Do Users Want?. In Persuasive Technology, Shlomo Berkovsky and Jill Freyne (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 15–26.Google ScholarGoogle Scholar
  4. John Brooke. 1996. SUS: a “quick and dirty” usability scale. Usability evaluation in industry 1 (1996), 189.Google ScholarGoogle Scholar
  5. Nico Castelli, Corinna Ogonowski, Timo Jakobi, Martin Stein, Gunnar Stevens, and Volker Wulf. 2017. What Happened in My Home? An End-User Development Approach for Smart Home Data Visualization. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 853–866. https://doi.org/10.1145/3025453.3025485Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Yuxin Chen, Huiying Li, Shan-Yuan Teng, Steven Nagels, Zhijing Li, Pedro Lopes, Ben Y. Zhao, and Haitao Zheng. 2020. Wearable Microphone Jamming. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3313831.3376304Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Youngjun Cho, Nadia Bianchi-Berthouze, Nicolai Marquardt, and Simon J. Julier. 2018. Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3173574.3173576Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. R. Chow. 2017. The Last Mile for IoT Privacy. IEEE Security & Privacy 15, 6 (2017), 73–76. https://doi.org/10.1109/MSP.2017.4251118Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Richard Chow, Serge Egelman, Raghudeep Kannavara, Hosub Lee, Suyash Misra, and Edward Wang. 2015. HCI in Business: A Collaboration with Academia in IoT Privacy. In HCI in Business, Fiona Fui-Hoon Nah and Chuan-Hoo Tan (Eds.). Springer International Publishing, Cham, 679–687.Google ScholarGoogle Scholar
  10. Hyunji Chung, Michaela Iorga, Jeffrey Voas, and Sangjin Lee. 2017. Alexa, Can I Trust You?Computer 50, 9 (2017), 100–104. https://doi.org/10.1109/MC.2017.3571053Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Jessica Colnago, Yuanyuan Feng, Tharangini Palanivel, Sarah Pearman, Megan Ung, Alessandro Acquisti, Lorrie Faith Cranor, and Norman Sadeh. 2020. Informing the Design of a Personalized Privacy Assistant for the Internet of Things. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376389Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Mary J. Culnan and Pamela K. Armstrong. 1999. Information Privacy Concerns, Procedural Fairness, and Impersonal Trust: An Empirical Investigation. Organization Science 10, 1 (1999), 104–115. https://doi.org/10.1287/orsc.10.1.104Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. P. Emami-Naeini, Y. Agarwal, L. Faith Cranor, and H. Hibshi. 2020. Ask the Experts: What Should Be on an IoT Privacy and Security Label?. In 2020 IEEE Symposium on Security and Privacy (SP) (San Francisco, CA, USA). IEEE, New York, NY, USA, 447–464. https://doi.org/10.1109/SP40000.2020.00043Google ScholarGoogle ScholarCross RefCross Ref
  14. Pardis Emami-Naeini, Sruti Bhagavatula, Hana Habib, Martin Degeling, Lujo Bauer, Lorrie Cranor, and Norman Sadeh. 2017. Privacy Expectations and Preferences in an IoT World. In Proceedings of the Symposium on Usable Privacy and Security(SOUPS ’17). USENIX Association, Berkeley, CA, USA, 399–412.Google ScholarGoogle Scholar
  15. Pardis Emami Naeini, Martin Degeling, Lujo Bauer, Richard Chow, Lorrie Faith Cranor, Mohammad Reza Haghighat, and Heather Patterson. 2018. The Influence of Friends and Experts on Privacy Decision Making in IoT Scenarios. Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 48 (Nov. 2018), 26 pages. https://doi.org/10.1145/3274317Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Pardis Emami-Naeini, Henry Dixon, Yuvraj Agarwal, and Lorrie Faith Cranor. 2019. Exploring How Privacy and Security Factor into IoT Device Purchase Behavior. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, Article 534, 12 pages. https://doi.org/10.1145/3290605.3300764Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Markus Funk, Robin Boldt, Bastian Pfleging, Max Pfeiffer, Niels Henze, and Albrecht Schmidt. 2014. Representing Indoor Location of Objects on Wearable Computers with Head-Mounted Displays. In Proceedings of the 5th Augmented Human International Conference (Kobe, Japan) (AH ’14). Association for Computing Machinery, New York, NY, USA, Article 18, 4 pages. https://doi.org/10.1145/2582051.2582069Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Christine Geeng and Franziska Roesner. 2019. Who’s In Control? Interactions In Multi-User Smart Homes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3290605.3300498Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Nina Gerber, Paul Gerber, and Melanie Volkamer. 2018. Explaining the privacy paradox: A systematic review of literature investigating privacy attitude and behavior. Computers & Security 77(2018), 226 – 261. https://doi.org/10.1016/j.cose.2018.04.002Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Hamza Harkous, Kassem Fawaz, Kang G. Shin, and Karl Aberer. 2016. PriBots: Conversational Privacy with Chatbots. In Twelfth Symposium on Usable Privacy and Security (SOUPS 2016). USENIX Association, Denver, CO, 6 pages. https://www.usenix.org/conference/soups2016/workshop-program/wfpn/presentation/harkousGoogle ScholarGoogle Scholar
  21. Sandra G. Hart. 2006. Nasa-Task Load Index (NASA-TLX); 20 Years Later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 50, 9(2006), 904–908. https://doi.org/10.1177/154193120605000909Google ScholarGoogle ScholarCross RefCross Ref
  22. Patrick Gage Kelley, Joanna Bresee, Lorrie Faith Cranor, and Robert W. Reeder. 2009. A ”Nutrition Label” for Privacy. In Proceedings of the 5th Symposium on Usable Privacy and Security (Mountain View, California, USA) (SOUPS ’09). Association for Computing Machinery, New York, NY, USA, Article 4, 12 pages. https://doi.org/10.1145/1572532.1572538Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Agnieszka Kitkowska, Mark Warner, Yefim Shulman, Erik Wästlund, and Leonardo A. Martucci. 2020. Enhancing Privacy through the Visual Design of Privacy Notices: Exploring the Interplay of Curiosity, Control and Affect. In Sixteenth Symposium on Usable Privacy and Security (SOUPS 2020). USENIX Association, Berkeley, CA, USA, 437–456. https://www.usenix.org/conference/soups2020/presentation/kitkowskaGoogle ScholarGoogle Scholar
  24. Predrag Klasnja, Sunny Consolvo, Tanzeem Choudhury, Richard Beckwith, and Jeffrey Hightower. 2009. Exploring Privacy Concerns about Personal Sensing. In Pervasive Computing, Hideyuki Tokuda, Michael Beigl, Adrian Friday, A. J. Bernheim Brush, and Yoshito Tobe (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 176–183.Google ScholarGoogle Scholar
  25. Tobias Kowatsch and Wolfgang Maass. 2012. Privacy Concerns and Acceptance of IoT Services. In The Internet of Things 2012 : New Horizons. IERC - Internet of Things European Research Cluster, Halifax, UK, 176–187. https://www.alexandria.unisg.ch/212316/Google ScholarGoogle Scholar
  26. Josephine Lau, Benjamin Zimmerman, and Florian Schaub. 2018. Alexa, Are You Listening?: Privacy Perceptions, Concerns and Privacy-Seeking Behaviors With Smart Speakers. Proceedings of the ACM Conference on Human-Computer Interaction 2, CSCW(2018), 102. https://doi.org/10.1145/3274371Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Scott Lederer, Anind K. Dey, and Jennifer Mankoff. 2002. A Conceptual Model and a Metaphor of Everyday Privacy in Ubiquitous. Technical Report. University of California at Berkeley, USA.Google ScholarGoogle Scholar
  28. Scott Lederer, Jennifer Mankoff, and Anind K. Dey. 2003. Who Wants to Know What When? Privacy Preference Determinants in Ubiquitous Computing. In CHI ’03 Extended Abstracts on Human Factors in Computing Systems (Ft. Lauderdale, Florida, USA) (CHI EA ’03). Association for Computing Machinery, New York, NY, USA, 724–725. https://doi.org/10.1145/765891.765952Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. H. Lee and A. Kobsa. 2016. Understanding user privacy in Internet of Things environments. In 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT) (Reston, VA, USA). IEEE, New York, NY, USA, 407–412.Google ScholarGoogle Scholar
  30. H. Lee and A. Kobsa. 2017. Privacy preference modeling and prediction in a simulated campuswide IoT environment. In 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom)(Kona, HI, USA). IEEE, New York, NY, USA, 276–285.Google ScholarGoogle Scholar
  31. Naresh K. Malhotra, Sung S. Kim, and James Agarwal. 2004. Internet Users’ Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model. Information Systems Research 15, 4 (2004), 336–355. https://doi.org/10.1287/isre.1040.0032Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Shrirang Mare, Franziska Roesner, and Tadayoshi Kohno. 01 Apr. 2020. Smart Devices in Airbnbs: Considering Privacy and Security for both Guests and Hosts. Proceedings on Privacy Enhancing Technologies 2020, 2 (01 Apr. 2020), 436 – 458. https://doi.org/10.2478/popets-2020-0035Google ScholarGoogle ScholarCross RefCross Ref
  33. Davit Marikyan, Savvas Papagiannidis, and Eleftherios Alamanos. 2019. A systematic review of the smart home literature: A user perspective. Technological Forecasting and Social Change 138 (2019), 139 – 154. https://doi.org/10.1016/j.techfore.2018.08.015Google ScholarGoogle ScholarCross RefCross Ref
  34. Karola Marky, Sarah Prange, Florian Krell, Max Mühlhäuser, and Florian Alt. 2020. “You Just Can’t Know about Everything”: Privacy Perceptions of Smart Home Visitors. In 19th International Conference on Mobile and Ubiquitous Multimedia (Essen, Germany) (MUM 2020). Association for Computing Machinery, New York, NY, USA, 83–95. https://doi.org/10.1145/3428361.3428464Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Karola Marky, Alexandra Voit, Alina Stöver, Kai Kunze, Svenja Schröder, and Max Mühlhäuser. 2020. ”I Don’t Know How to Protect Myself”: Understanding Privacy Perceptions Resulting from the Presence of Bystanders in Smart Environments. In Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society (Tallinn, Estonia) (NordiCHI ’20). Association for Computing Machinery, New York, NY, USA, Article 4, 11 pages. https://doi.org/10.1145/3419249.3420164Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Simon Mayer, Yassin N. Hassan, and Gábor Sörös. 2014. A Magic Lens for Revealing Device Interactions in Smart Environments. In SIGGRAPH Asia 2014 Mobile Graphics and Interactive Applications (Shenzhen, China) (SA ’14). Association for Computing Machinery, New York, NY, USA, Article 9, 6 pages. https://doi.org/10.1145/2669062.2669077Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. M Granger Morgan, Baruch Fischhoff, Ann Bostrom, Cynthia J Atman, 2002. Risk communication: A mental models approach. Cambridge University Press, Cambridge, United Kingdom.Google ScholarGoogle Scholar
  38. David H. Nguyen, Alfred Kobsa, and Gillian R. Hayes. 2008. An Empirical Investigation of Concerns of Everyday Tracking and Recording Technologies. Association for Computing Machinery, New York, NY, USA, 182–191. https://doi.org/10.1145/1409635.1409661Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Helen Nissenbaum. 2004. Privacy as contextual integrity. Wash. L. Rev. 79(2004), 119.Google ScholarGoogle Scholar
  40. Helen Nissenbaum. 2009. Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press, Stanford, CA, USA.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Rebecca S. Portnoff, Linda N. Lee, Serge Egelman, Pratyush Mishra, Derek Leung, and David Wagner. 2015. Somebody’s Watching Me? Assessing the Effectiveness of Webcam Indicator Lights. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15). Association for Computing Machinery, New York, NY, USA, 1649–1658. https://doi.org/10.1145/2702123.2702164Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. I. Psychoula, D. Singh, L. Chen, F. Chen, A. Holzinger, and H. Ning. 2018. Users’ Privacy Concerns in IoT Based Applications. In 2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (Guangzhou, China). IEEE, New York, NY, USA, 1887–1894.Google ScholarGoogle Scholar
  43. Joseph Redmon and Ali Farhadi. 2018. YOLOv3: An Incremental Improvement. arxiv:1804.02767 [cs.CV]Google ScholarGoogle Scholar
  44. Florian Schaub, Rebecca Balebako, Adam L. Durity, and Lorrie Faith Cranor. 2015. A Design Space for Effective Privacy Notices. In Eleventh Symposium On Usable Privacy and Security (SOUPS 2015). USENIX Association, Ottawa, 1–17. https://www.usenix.org/conference/soups2015/proceedings/presentation/schaubGoogle ScholarGoogle Scholar
  45. Yunpeng Song, Yun Huang, Zhongmin Cai, and Jason I. Hong. 2020. I’m All Eyes and Ears: Exploring Effective Locators for Privacy Awareness in IoT Scenarios. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376585Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Madiha Tabassum, Tomasz Kosiński, and Heather Richter Lipford. 2019. “I don’t own the data”: End User Perceptions of Smart Home Device Data Practices and Risks. In Proceedings of the Fifteenth USENIX Conference on Usable Privacy and Security (Santa Clara, CA, USA) (SOUPS’19). USENIX Association, Berkeley, CA, USA, 435–450.Google ScholarGoogle Scholar
  47. Christian Tiefenau, Maximilian Häring, Eva Gerlitz, and Emanuel von Zezschwitz. 2019. Making Privacy Graspable: Can we Nudge Users to use Privacy Enhancing Techniques?arxiv:1911.07701 [cs.HC]Google ScholarGoogle Scholar
  48. T. Franklin Waddell, Joshua R. Auriemma, and S. Shyam Sundar. 2016. Make It Simple, or Force Users to Read? Paraphrased Design Improves Comprehension of End User License Agreements. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA, 5252–5256. https://doi.org/10.1145/2858036.2858149Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Mark Weiser. 1991. The Computer for the 21 st Century. Scientific American 265, 3 (1991), 94–105. http://www.jstor.org/stable/24938718Google ScholarGoogle ScholarCross RefCross Ref
  50. M. Weiser, R. Gold, and J. S. Brown. 1999. The origins of ubiquitous computing research at PARC in the late 1980s. IBM Systems Journal 38, 4 (1999), 693–696.Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Gary White, Christian Cabrera, Andrei Palade, and Siobhán Clarke. 2019. Augmented Reality in IoT. In Service-Oriented Computing – ICSOC 2018 Workshops, Xiao Liu, Michael Mrissa, Liang Zhang, Djamal Benslimane, Aditya Ghose, Zhongjie Wang, Antonio Bucchiarone, Wei Zhang, Ying Zou, and Qi Yu (Eds.). Springer International Publishing, Cham, 149–160.Google ScholarGoogle Scholar
  52. EJ Williams. 1949. Experimental designs balanced for the estimation of residual effects of treatments. Australian Journal of Chemistry 2, 2 (1949), 149–168.Google ScholarGoogle ScholarCross RefCross Ref
  53. Yaxing Yao, Justin Reed Basdeo, Smirity Kaushik, and Yang Wang. 2019. 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 (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3290605.3300428Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Yaxing Yao, Justin Reed Basdeo, Oriana Rosata Mcdonough, and Yang Wang. 2019. Privacy Perceptions and Designs of Bystanders in Smart Homes. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 59 (Nov. 2019), 24 pages. https://doi.org/10.1145/3359161Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Eric Zeng and Franziska Roesner. 2019. Understanding and Improving Security and Privacy in Multi-User Smart Homes: A Design Exploration and In-Home User Study. In 28th USENIX Security Symposium (USENIX Security 19). USENIX Association, Santa Clara, CA, 159–176. https://www.usenix.org/conference/usenixsecurity19/presentation/zengGoogle ScholarGoogle Scholar

Index Terms

  1. PriView– Exploring Visualisations to Support Users’ Privacy Awareness
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
      May 2021
      10862 pages
      ISBN:9781450380966
      DOI:10.1145/3411764

      Copyright © 2021 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 7 May 2021

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

      Acceptance Rates

      Overall Acceptance Rate6,199of26,314submissions,24%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format