Skip to main content

Improving the Representation Choices of Privacy Policies for End-Users

  • Conference paper
  • First Online:
Web Information Systems and Technologies (WEBIST 2022)

Abstract

Privacy policies provide users the possibility to get informed about how their data are being used by specific services and vendors. Unfortunately their texts are usually long and users are not devoting the required time to read them and understand their content. Tools that bring the privacy policies closer to the users can assist towards enhancing users’ privacy awareness. In this work, we are presenting the updated version of Privacy Policy Beautifier, our approach and accompanying tool that offers various representations of the privacy policy text, as a way to assist the users in better understanding the policy, devoting less time to explore its main content. Text highlighting, text summarization, word cloud, GDPR terms presence/absence are the techniques employed for the representations. The updated version of Privacy Policy Beautifier has been evaluated for its enhanced features via the participation of 32 users with promising results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://openai.com/.

  2. 2.

    https://forms.gle/bjARtmjkuZjSYkveA.

References

  1. Angulo, J., Fischer-Hübner, S., Wästlund, E., Pulls, T.: Towards usable privacy policy display and management. Inf. Manag. Comput. Secur. 20(1), 4–17 (2012)

    Article  Google Scholar 

  2. Bangor, A., Kortum, P., Miller, J.: Determining what individual SUS scores mean: adding an adjective rating scale. J. Usability Stud. 4(3), 114–123 (2009)

    Google Scholar 

  3. Bhatia, J., Breaux, T.D., Reidenberg, J.R., Norton, T.B.: A theory of vagueness and privacy risk perception. In: 2016 IEEE 24th International Requirements Engineering Conference (RE), pp. 26–35. IEEE (2016)

    Google Scholar 

  4. Brooke, J., et al.: SUS-a quick and dirty usability scale. Usability Eval. Ind. 189(194), 4–7 (1996)

    Google Scholar 

  5. Dhar, A., Mukherjee, H., Dash, N.S., Roy, K.: Text categorization: past and present. Artif. Intell. Rev. 54(4), 3007–3054 (2021)

    Article  Google Scholar 

  6. Feldt, R., Magazinius, A.: Validity threats in empirical software engineering research-an initial survey. In: SEKE, pp. 374–379 (2010)

    Google Scholar 

  7. Gardner, M.W., Dorling, S.: Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998)

    Article  Google Scholar 

  8. Harkous, H., Fawaz, K., Lebret, R., Schaub, F., Shin, K.G., Aberer, K.: Polisis: automated analysis and presentation of privacy policies using deep learning. In: 27th \(\{\)USENIX\(\}\) Security Symposium (\(\{\)USENIX\(\}\) Security 2018), pp. 531–548 (2018)

    Google Scholar 

  9. Kaili, M., Kapitsaki, G.M.: Privacy policy beautifier: Bringing privacy policies closer to users. In: Decker, S., Mayo, F.J.D., Marchiori, M., Filipe, J. (eds.) Proceedings of the 18th International Conference on Web Information Systems and Technologies, WEBIST 2022, Valletta, Malta, 25–27 October 2022, pp. 54–63. SCITEPRESS (2022). https://doi.org/10.5220/0011541600003318,

  10. Kelley, P.G., Bresee, J., Cranor, L.F., Reeder, R.W.: A “nutrition label” for privacy. In: Proceedings of the 5th Symposium on Usable Privacy and Security, pp. 1–12 (2009)

    Google Scholar 

  11. Kim, K., Ko, S., Elmqvist, N., Ebert, D.S.: Wordbridge: using composite tag clouds in node-link diagrams for visualizing content and relations in text corpora. In: 2011 44th Hawaii International Conference on System Sciences, pp. 1–8. IEEE (2011)

    Google Scholar 

  12. Leung, K.M.: Naive Bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering 2007, pp. 123–156 (2007)

    Google Scholar 

  13. Lewis, J.R.: The system usability scale: past, present, and future. Int. J. Hum.-Comput. Interact. 34(7), 577–590 (2018)

    Article  Google Scholar 

  14. Liaw, A., Wiener, M., et al.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)

    Google Scholar 

  15. Linden, T., Khandelwal, R., Harkous, H., Fawaz, K.: The privacy policy landscape after the GDPR. arXiv preprint arXiv:1809.08396 (2018)

  16. Lund, B.D., Wang, T.: Chatting about ChatGPT: how may AI and GPT impact academia and libraries? Library Hi Tech News (2023)

    Google Scholar 

  17. Soumelidou, A., Tsohou, A.: Effects of privacy policy visualization on users’ information privacy awareness level: the case of Instagram. Inf. Technol. People 33(2), 502–534 (2019)

    Article  Google Scholar 

  18. Vanezi, E., Zampa, G., Mettouris, C., Yeratziotis, A., Papadopoulos, G.A.: CompLicy: evaluating the GDPR alignment of privacy policies - a study on web platforms. In: Cherfi, S., Perini, A., Nurcan, S. (eds.) RCIS 2021. LNBIP, vol. 415, pp. 152–168. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75018-3_10

    Chapter  Google Scholar 

  19. Wagner, C., Trenz, M., Veit, D.: How do habit and privacy awareness shape privacy decisions? (2020)

    Google Scholar 

  20. Wilson, S., et al.: The creation and analysis of a website privacy policy corpus. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1330–1340 (2016)

    Google Scholar 

  21. Wu, K.W., Huang, S.Y., Yen, D.C., Popova, I.: The effect of online privacy policy on consumer privacy concern and trust. Comput. Hum. Behav. 28(3), 889–897 (2012)

    Article  Google Scholar 

  22. Zaeem, R.N., German, R.L., Barber, K.S.: Privacycheck: automatic summarization of privacy policies using data mining. ACM Trans. Internet Technol. (TOIT) 18(4), 1–18 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Georgia M. Kapitsaki .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kaili, M., Kapitsaki, G.M. (2023). Improving the Representation Choices of Privacy Policies for End-Users. In: Marchiori, M., Domínguez Mayo, F.J., Filipe, J. (eds) Web Information Systems and Technologies. WEBIST 2022. Lecture Notes in Business Information Processing, vol 494. Springer, Cham. https://doi.org/10.1007/978-3-031-43088-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43088-6_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43087-9

  • Online ISBN: 978-3-031-43088-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics