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Look Before You Leap! Perceptions and Attitudes Towards Inferences in Wearable Fitness Trackers

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HCI for Cybersecurity, Privacy and Trust (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14045))

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Abstract

Users of fitness trackers regularly share their data with a variety of people and entities and do not consider this data as very sensitive. Yet, this data could be used to infer additional information, such as mood, health status, or even identity. We conducted interviews and a survey with fitness tracker users to examine their awareness and attitudes towards multiple inference scenarios. Our results demonstrate that participants have a higher willingness to share individual primary data over information inferred from that data, providing evidence that users are not considering potential inferences in their sharing decisions. Our findings also identify a number of factors related to users’ attitudes towards inferences.

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References

  1. CCS Insight (2018). https://www.ccsinsight.com/press/company-news/3695-success-of-apple-watch-means-more-growth-in-sales-of-wearable-technology/

  2. Hern, A. (2018). https://www.theguardian.com/world/2018/jan/28/fitness-tracking-app-gives-away-location-of-secret-us-army-bases

  3. Rader, E., Slaker, J.: The importance of visibility for folk theories of sensor data. In: Thirteenth Symposium on Usable Privacy and Security (\(\{\)SOUPS\(\}\) 2017), pp. 257–270 (2017)

    Google Scholar 

  4. Schneegass, S., Poguntke, R., Machulla, T.: Understanding the impact of information representation on willingness to share information. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–6 (2019)

    Google Scholar 

  5. Bilogrevic, I., Ortlieb, M.: “If you put all the pieces together...” attitudes towards data combination and sharing across services and companies. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 5215–5227 (2016)

    Google Scholar 

  6. Dolin, C., et al.: Unpacking perceptions of data-driven inferences underlying online targeting and personalization. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2018)

    Google Scholar 

  7. Ur, B., Leon, P.G., Cranor, L.F., Shay, R., Wang, Y.: Smart, useful, scary, creepy: perceptions of online behavioral advertising. In: Proceedings of the Eighth Symposium on Usable Privacy and Security, pp. 1–15 (2012)

    Google Scholar 

  8. Rader, E., Hautea, S., Munasinghe, A.: “I have a narrow thought process”: constraints on explanations connecting inferences and self-perceptions. In: Sixteenth Symposium on Usable Privacy and Security (\(\{\)SOUPS\(\}\) 2020), pp. 457–488 (2020)

    Google Scholar 

  9. Aktypi, A., Nurse, J.R., Goldsmith, M.: Unwinding Ariadne’s identity thread: privacy risks with fitness trackers and online social networks. In: Proceedings of the 2017 on Multimedia Privacy and Security, pp. 1–11 (2017)

    Google Scholar 

  10. Alqhatani, A., Lipford, H.R.: “There is nothing that i need to keep secret”: sharing practices and concerns of wearable fitness data. In: Fifteenth Symposium on Usable Privacy and Security (\(\{\)SOUPS\(\}\) 2019) (2019)

    Google Scholar 

  11. Gabriele, S., Chiasson, S.: Understanding fitness tracker users’ security and privacy knowledge, attitudes and behaviours. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2020)

    Google Scholar 

  12. Motti, V.G., Caine, K.: Users’ privacy concerns about wearables. In: Brenner, M., Christin, N., Johnson, B., Rohloff, K. (eds.) FC 2015. LNCS, vol. 8976, pp. 231–244. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48051-9_17

    Chapter  Google Scholar 

  13. Vitak, J., Liao, Y., Kumar, P., Zimmer, M., Kritikos, K.: Privacy attitudes and data valuation among fitness tracker users. In: Chowdhury, G., McLeod, J., Gillet, V., Willett, P. (eds.) iConference 2018. LNCS, vol. 10766, pp. 229–239. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78105-1_27

    Chapter  Google Scholar 

  14. Hautea, S., Munasinghe, A., Rader, E.: ‘That’s not me’: surprising algorithmic inferences. In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–7 (2020)

    Google Scholar 

  15. Thomaz, E., Essa, I., Abowd, G.D.: A practical approach for recognizing eating moments with wrist-mounted inertial sensing. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 1029–1040 (2015)

    Google Scholar 

  16. Kröger, J.L., Raschke, P., Bhuiyan, T.R.: Privacy implications of accelerometer data: a review of possible inferences. In: Proceedings of the 3rd International Conference on Cryptography, Security and Privacy, pp. 81–87 (2019)

    Google Scholar 

  17. Meteriz, Ü., Yıldıran, N.F., Mohaisen, A.: You can run, but you cannot hide: using elevation profiles to breach location privacy through trajectory prediction. arXiv preprint arXiv:1910.09041 (2019)

  18. Warshaw, J., Taft, N., Woodruff, A.: Intuitions, analytics, and killing ants: inference literacy of high school-educated adults in the \(\{\)US\(\}\). In: Twelfth Symposium on Usable Privacy and Security (\(\{\)SOUPS\(\}\) 2016), pp. 271–285 (2016)

    Google Scholar 

  19. Lupton, D.: Quantified sex: a critical analysis of sexual and reproductive self-tracking using apps. Cult. Health Sex. 17(4), 440–453 (2015)

    Article  Google Scholar 

  20. Peppet, S.R.: Regulating the internet of things: first steps toward managing discrimination, privacy, security and consent. Tex. L. Rev. 93, 85 (2014)

    Google Scholar 

  21. Kröger, J.: Unexpected inferences from sensor data: a hidden privacy threat in the internet of things. In: Strous, L., Cerf, V.G. (eds.) IFIPIoT 2018. IAICT, vol. 548, pp. 147–159. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15651-0_13

    Chapter  Google Scholar 

  22. Rader, E.: Awareness of behavioral tracking and information privacy concern in Facebook and Google. In: 10th Symposium on Usable Privacy and Security (\(\{\)SOUPS\(\}\) 2014), pp. 51–67 (2014)

    Google Scholar 

  23. Weinshel, B., et al.: 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, pp. 149–166 (2019)

    Google Scholar 

  24. Gorm, N., Shklovski, I.: Sharing steps in the workplace: changing privacy concerns over time. In: proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 4315–4319 (2016)

    Google Scholar 

  25. Ziegeldorf, J.H., Morchon, O.G., Wehrle, K.: Privacy in the internet of things: threats and challenges. Secur. Commun. Netw. 7(12), 2728–2742 (2014)

    Article  Google Scholar 

  26. Wagner, I., He, Y., Rosenberg, D., Janicke, H.: User interface design for privacy awareness in eHealth technologies. In: 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 38–43. IEEE (2016)

    Google Scholar 

  27. Zimmer, M., Kumar, P., Vitak, J., Liao, Y., Chamberlain Kritikos, K.: ‘There’s nothing really they can do with this information’: unpacking how users manage privacy boundaries for personal fitness information. Inf. Commun. Soc. 23(7), 1020–1037 (2020)

    Article  Google Scholar 

  28. Naeini, P.E., et al.: Privacy expectations and preferences in an IoT world. In: Thirteenth Symposium on Usable Privacy and Security (\(\{\)SOUPS\(\}\) 2017), pp. 399–412 (2017)

    Google Scholar 

  29. Paul, G., Irvine, J.: Privacy implications of wearable health devices. In: Proceedings of the 7th International Conference on Security of Information and Networks, pp. 117–121 (2014)

    Google Scholar 

  30. Rao, L.: (2011). https://techcrunch.com/2011/07/03/sexual-activity-tracked-by-fitbit-shows-up-in-google-search-results/

  31. Smith, H.J., Dinev, T., Xu, H.: Information privacy research: an interdisciplinary review. MIS Q. 989–1015 (2011)

    Google Scholar 

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Correspondence to Abdulmajeed Alqhatani .

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Alqhatani, A., Lipford, H.R. (2023). Look Before You Leap! Perceptions and Attitudes Towards Inferences in Wearable Fitness Trackers. In: Moallem, A. (eds) HCI for Cybersecurity, Privacy and Trust. HCII 2023. Lecture Notes in Computer Science, vol 14045. Springer, Cham. https://doi.org/10.1007/978-3-031-35822-7_27

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  • DOI: https://doi.org/10.1007/978-3-031-35822-7_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35821-0

  • Online ISBN: 978-3-031-35822-7

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