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Towards a Conceptual Model for Provoking Privacy Speculation

Published:25 April 2020Publication History

ABSTRACT

The proliferation of ubiquitous computing introduces several challenges to user privacy. Data from multiple sensors and users is aggregated at various scales to produce new, fine-grained inferences about people. Users of these systems are asked to consent to sharing their data without full knowledge of what data are recorded, how the data are used, who has access to the data, and most importantly risks associated with sharing. Recent work has shown that provoking privacy speculation among system users, by visualizing these various aspects, improves user knowledge and enables them to make informed decisions about their data. This paper presents a conceptual model of how researchers can make inferences that provoke privacy speculation among system users and a case study applying the model.

References

  1. R. Beckwith. 2003. Designing for ubiquity: the perception of privacy. IEEE Pervasive Computing 2, 2 (April 2003), 40--46. http://dx.doi.org/10.1109/MPRV.2003.1203752Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Giovanni Iachello, Khai N. Truong, Gregory D. Abowd, Gillian R. Hayes, and Molly Stevens. 2006. Prototyping and Sampling Experience to Evaluate Ubiquitous Computing Privacy in the Real World. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '06). ACM, New York, NY, USA, 1009--1018. http://dx.doi.org/10.1145/1124772.1124923Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. I. A. Junglas and C. Spitzmuller. 2005. A Research Model for Studying Privacy Concerns Pertaining to Location-Based Services. In Proceedings of the 38th Annual Hawaii International Conference on System Sciences. 180b--180b. http://dx.doi.org/10.1109/HICSS.2005.47Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. 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 ScholarDigital LibraryDigital Library
  5. Delfina Malandrino, Andrea Petta, Vittorio Scarano, Luigi Serra, Raffaele Spinelli, and Balachander Krishnamurthy. 2013. Privacy Awareness About Information Leakage: Who Knows What About Me?. In Proceedings of the 12th ACM Workshop on Workshop on Privacy in the Electronic Society (WPES '13). ACM, New York, NY, USA, 279--284. http://dx.doi.org/10.1145/2517840.2517868Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Aleecia M McDonald and Lorrie Faith Cranor. 2008. The cost of reading privacy policies. Isjlp 4 (2008), 543.Google ScholarGoogle Scholar
  7. Vivian Genaro Motti and Kelly Caine. 2015. Users' Privacy Concerns About Wearables. In Financial Cryptography and Data Security, Michael Brenner, Nicolas Christin, Benjamin Johnson, and Kurt Rohloff (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 231--244.Google ScholarGoogle Scholar
  8. David H. Nguyen, Alfred Kobsa, and Gillian R. Hayes. 2008. An Empirical Investigation of Concerns of Everyday Tracking and Recording Technologies. In Proceedings of the 10th International Conference on Ubiquitous Computing (UbiComp '08). ACM, New York, NY, USA, 182--191. http://dx.doi.org/10.1145/1409635.1409661Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Andrew Odlyzko. 2003. Privacy, Economics, and Price Discrimination on the Internet. In Proceedings of the 5th International Conference on Electronic Commerce (ICEC '03). ACM, New York, NY, USA, 355--366. http://dx.doi.org/10.1145/948005.948051Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Deger Ozkaramanli, Peter MA Desmet, Peter Lloyd, and Erik Bohemia. 2016. Provocative design for unprovocative designers: Strategies for triggering personal dilemmas. In Proceedings of Design Research Society 50th Anniversary Conference. 1--16.Google ScholarGoogle ScholarCross RefCross Ref
  11. Scott R Peppet. 2014. Regulating the internet of things: first steps toward managing discrimination, privacy, security and consent. Tex. L. Rev. 93 (2014), 85.Google ScholarGoogle Scholar
  12. Stefanie Pötzsch. 2009. Privacy Awareness: A Means to Solve the Privacy Paradox?. In The Future of Identity in the Information Society, Vashek Matyávs, Simone Fischer-Hübner, Daniel Cvrvc ek, and Petr vS venda (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 226--236.Google ScholarGoogle Scholar
  13. Blaine A. Price, Karim Adam, and Bashar Nuseibeh. 2005. Keeping ubiquitous computing to yourself: A practical model for user control of privacy. International Journal of Human-Computer Studies 63, 1 (2005), 228 -- 253. http://dx.doi.org/10.1016/j.ijhcs.2005.04.008Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Emilee Rader and Janine Slaker. 2017. The importance of visibility for folk theories of sensor data. In Thirteenth Symposium on Usable Privacy and Security (SOUPS 2017). USENIX Association, Santa Clara, CA, 257--270. https://www.usenix.org/conference/soups2017/technical-sessions/presentation/raderGoogle ScholarGoogle Scholar
  15. Jingjing Ren, Daniel J. Dubois, David Choffnes, Anna Maria Mandalari, Roman Kolcun, and Hamed Haddadi. 2019. Information Exposure From Consumer IoT Devices: A Multidimensional, Network-Informed Measurement Approach. In Proceedings of the Internet Measurement Conference (IMC '19). Association for Computing Machinery, New York, NY, USA, 267--279. http://dx.doi.org/10.1145/3355369.3355577Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Irina Shklovski, Scott D. Mainwaring, Halla Hrund Skúladóttir, and Höskuldur Borgthorsson. 2014. Leakiness and Creepiness in App Space: Perceptions of Privacy and Mobile App Use. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '14). Association for Computing Machinery, New York, NY, USA, 2347--2356. http://dx.doi.org/10.1145/2556288.2557421Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Nili Steinfeld. 2016. “I agree to the terms and conditions”: (How) do users read privacy policies online? An eye-tracking experiment. Computers in Human Behavior 55 (2016), 992 -- 1000. http://dx.doi.org/10.1016/j.chb.2015.09.038Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Liesbet van Zoonen. 2016. Privacy concerns in smart cities. Government Information Quarterly 33, 3 (2016), 472 -- 480. http://dx.doi.org/10.1016/j.giq.2016.06.004Google ScholarGoogle ScholarCross RefCross Ref
  19. Ben Weinshel, Miranda Wei, Mainack Mondal, Euirim Choi, Shawn Shan, Claire Dolin, Michelle L. Mazurek, and Blase Ur. 2019. Oh, the Places You'Ve Been! User Reactions to Longitudinal Transparency About Third-Party Web Tracking and Inferencing. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS '19). ACM, New York, NY, USA, 149--166. http://dx.doi.org/10.1145/3319535.3363200Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
          April 2020
          4474 pages
          ISBN:9781450368193
          DOI:10.1145/3334480

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          Publication History

          • Published: 25 April 2020

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