Skip to main content

A Study on User Preference: Influencing App Selection Decision with Privacy Indicator

  • Conference paper
  • First Online:
HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12423))

Included in the following conference series:

Abstract

This paper investigates how the use of privacy indicators in app stores can influence user behavior, and if the added information can improve consumer transparency. After a pre-study on the design and symbology, a visual privacy indicator was implemented and evaluated in an app market prototype. A total of 82 participants were asked to select a number of task-specific apps. By varying the degrees of participatory background information, we show that impact of a privacy indicator on app selection behavior has statistical significance and such privacy preserving behavior can be invoked by mere presence of the indicator.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • 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.

    Before the event (data disclosure) takes place.

References

  1. Acquisti, A.: Nudging privacy: the behavioral economics of personal information. IEEE Secur. Priv. 7(6), 82–85 (2009)

    Article  Google Scholar 

  2. Acquisti, A., et al.: Nudges for privacy and security: understanding and assisting users’ choices online. ACM Comput. Surv. 50(3), 44:1–44:41 (2017). https://doi.org/10.1145/3054926. http://doi.acm.org/10.1145/3054926

    Article  Google Scholar 

  3. Acquisti, A., Brandimarte, L., Loewenstein, G.: Privacy and human behavior in the age of information. Science 347(6221), 509–514 (2015)

    Article  Google Scholar 

  4. Alohaly, M., Takabi, H.: Better privacy indicators: a new approach to quantification of privacy policies. In: Twelfth Symposium on Usable Privacy and Security (\(\{\)SOUPS\(\}\) 2016) (2016)

    Google Scholar 

  5. Balebako, R., Jung, J., Lu, W., Cranor, L.F., Nguyen, C.: Little brothers watching you: raising awareness of data leaks on smartphones. In: Proceedings of the Ninth Symposium on Usable Privacy and Security, p. 12. ACM (2013)

    Google Scholar 

  6. Benbasat, I., Dexter, A.S., Todd, P.: An experimental program investigating color-enhanced and graphical information presentation: an integration of the findings. Commun. ACM 29(11), 1094–1105 (1986)

    Article  Google Scholar 

  7. Christ, R.E.: Review and analysis of color coding research for visual displays. Hum. Factors 17(6), 542–570 (1975)

    Article  Google Scholar 

  8. Cimbalo, R.S., Beck, K.L., Sendziak, D.S.: Emotionally toned pictures and color selection for children and college students. J. Genet. Psychol. 133(2), 303–304 (1978)

    Article  Google Scholar 

  9. European Commission: Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation). Off J Eur Union p. L119 (2016)

    Google Scholar 

  10. Conover, W.J., Iman, R.L.: Rank transformations as a bridge between parametric and nonparametric statistics. Am. Stat. 35(3), 124–129 (1981)

    MATH  Google Scholar 

  11. Android Developers Documentation: Android 9.0 changes. https://developer.android.com/about/versions/pie/android-9.0-changes-all (2019). Accessed 12 Sept 2019

  12. Android Developers Documentation: Dangerous permissions (2019). Accessed 12 Sept 2019

    Google Scholar 

  13. Android Developers Documentation: Permissions overview. https://www.developer.android.com/guide/topics/permissions/overview (2019). Accessed 12 Sept 2019

  14. Android Developers Documentation: Runtime permissions. https://developer.android.com/distribute/best-practices/develop/runtime-permissions (2019). Accessed 12 Sept 2019

  15. Felt, A.P., Ha, E., Egelman, S., Haney, A., Chin, E., Wagner, D.: Android permissions: Uuser attention, comprehension, and behavior. In: Proceedings of the Eighth Symposium on Usable Privacy and Security, p. 3. ACM (2012)

    Google Scholar 

  16. Fritsch, L., Momen, N.: Derived partial identities generated from app permissions. In: Open Identity Summit (OID) 2017. Gesellschaft für Informatik (2017)

    Google Scholar 

  17. Gross, R., Acquisti, A.: Information revelation and privacy in online social networks. In: Proceedings of the 2005 ACM Workshop on Privacy in the Electronic Society, pp. 71–80. ACM (2005)

    Google Scholar 

  18. Gu, J., Xu, Y.C., Xu, H., Zhang, C., Ling, H.: Privacy concerns for mobile app download: an elaboration likelihood model perspective. Decis. Support Syst. 94, 19–28 (2017)

    Article  Google Scholar 

  19. Hatamian, M., Momen, N., Fritsch, L., Rannenberg, K.: A multilateral privacy impact analysis method for android apps. In: Naldi, M., Italiano, G.F., Rannenberg, K., Medina, M., Bourka, A. (eds.) APF 2019. LNCS, vol. 11498, pp. 87–106. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21752-5_7

    Chapter  Google Scholar 

  20. Hatamian, M., Serna, J., Rannenberg, K.: Revealing the unrevealed: mining smartphone users privacy perception on app markets. Comput. Secur. (2019). https://doi.org/10.1016/j.cose.2019.02.010. http://www.sciencedirect.com/science/article/pii/S0167404818313051

  21. Hatamian, M., Serna, J., Rannenberg, K., Igler, B.: FAIR: fuzzy alarming index rule for privacy analysis in smartphone apps. In: Lopez, J., Fischer-Hübner, S., Lambrinoudakis, C. (eds.) TrustBus 2017. LNCS, vol. 10442, pp. 3–18. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64483-7_1

    Chapter  Google Scholar 

  22. Hibbard, J.H., Peters, E.: Supporting informed consumer health care decisions: data presentation approaches that facilitate the use of information in choice. Annu. Rev. Public Health 24(1), 413–433 (2003)

    Article  Google Scholar 

  23. Jung, J., Han, S., Wetherall, D.: Short paper: enhancing mobile application permissions with runtime feedback and constraints. In: Proceedings of the Second ACM Workshop on Security and Privacy in Smartphones and Mobile Devices, pp. 45–50. ACM (2012)

    Google Scholar 

  24. 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 SOUPS 2009, pp. 4:1–4:12. ACM, New York (2009). https://doi.org/10.1145/1572532.1572538. http://doi.acm.org/10.1145/1572532.1572538

  25. Kelley, P.G., Consolvo, S., Cranor, L.F., Jung, J., Sadeh, N., Wetherall, D.: A conundrum of permissions: installing applications on an android smartphone. In: Blyth, J., Dietrich, S., Camp, L.J. (eds.) FC 2012. LNCS, vol. 7398, pp. 68–79. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34638-5_6

    Chapter  Google Scholar 

  26. Kelley, P.G., Cranor, L.F., Sadeh, N.: Privacy as part of the app decision-making process. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3393–3402. ACM (2013)

    Google Scholar 

  27. Kraus, L., Wechsung, I., Möller, S.: Using statistical information to communicate android permission risks to users. In: 2014 Workshop on Socio-Technical Aspects in Security and Trust, pp. 48–55. IEEE (2014)

    Google Scholar 

  28. Kruskal, W.H., Wallis, W.A.: Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47(260), 583–621 (1952)

    Article  Google Scholar 

  29. Lilliefors, H.W.: On the kolmogorov-smirnov test for normality with mean and variance unknown. J. Am. Stat. Assoc. 62(318), 399–402 (1967)

    Article  Google Scholar 

  30. McKnight, P.E., Najab, J.: Mann-whitney u test. In: The Corsini Encyclopedia of Psychology, p. 1 (2010)

    Google Scholar 

  31. Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2), 81 (1956)

    Article  Google Scholar 

  32. Momen, N.: Towards measuring apps’ privacy-friendliness. Ph.D. thesis, Karlstads Universitet (2018)

    Google Scholar 

  33. Momen, N., Bock, S., Fritsch, L.: Accept-maybe-decline: introducing partial consent for the permission-based access control model of android. In: Proceedings of the 25th ACM Symposium on Access Control Models and Technologies, pp. 71–80 (2020)

    Google Scholar 

  34. Momen, N., Fritsch, L.: App-generated digital identities extracted through android permission-based data access-a survey of app privacy. In: SICHERHEIT 2020 (2020)

    Google Scholar 

  35. Momen, N., Hatamian, M., Fritsch, L.: Did app privacy improve after the GDPR? IEEE Secur. Priv. 17(6), 10–20 (2019)

    Article  Google Scholar 

  36. Momen, N., Pulls, T., Fritsch, L., Lindskog, S.: How much privilege does an app need? Investigating resource usage of android apps. In: The Fifteenth International Conference on Privacy, Security and Trust-PST 2017, 28–30 August 2017, Calgary, Alberta, Canada. IEEE (2017)

    Google Scholar 

  37. Murmann, P., Fischer-Hübner, S.: Tools for achieving usable ex post transparency: a survey. IEEE Access 5, 22965–22991 (2017)

    Article  Google Scholar 

  38. Rajivan, P., Camp, J.: Influence of privacy attitude and privacy cue framing on android app choices. In: Twelfth Symposium on Usable Privacy and Security (SOUPS 2016). USENIX Association, Denver, CO, June 2016. https://www.usenix.org/conference/soups2016/workshop-program/wpi/presentation/rajivan

  39. Ramokapane, K.M., Mazeli, A.C., Rashid, A.: Skip, skip, skip, accept!!!: A study on the usability of smartphone manufacturer provided default features and user privacy. Proceedings on Privacy Enhancing Technologies 2019(2), 209–227 (2019)

    Article  Google Scholar 

  40. Rosen, S., Qian, Z., Mao, Z.M.: Appprofiler: a flexible method of exposing privacy-related behavior in android applications to end users. In: Proceedings of the Third ACM Conference on Data and Application Security and Privacy, pp. 221–232. ACM (2013)

    Google Scholar 

  41. 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 CHI 2019, pp. 523:1–523:6. ACM, New York (2019). https://doi.org/10.1145/3290605.3300753. http://doi.acm.org/10.1145/3290605.3300753

  42. Shih, F., Liccardi, I., Weitzner, D.: Privacy tipping points in smartphones privacy preferences. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems CHI 2015, pp. 807–816. ACM, New York (2015). https://doi.org/10.1145/2702123.2702404. http://doi.acm.org/10.1145/2702123.2702404

  43. Shklovski, I., Mainwaring, S.D., Skúladóttir, H.H., Borgthorsson, H.: Leakiness and creepiness in app space: perceptions of privacy and mobile app use. In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, pp. 2347–2356. ACM (2014)

    Google Scholar 

  44. Thompson, C., Johnson, M., Egelman, S., Wagner, D., King, J.: When it’s better to ask forgiveness than get permission: attribution mechanisms for smartphone resources. In: Proceedings of the Ninth Symposium on Usable Privacy and Security, p. 1. ACM (2013)

    Google Scholar 

  45. Watson, J., Lipford, H.R., Besmer, A.: Mapping user preference to privacy default settings. ACM Trans. Comput.-Hum. Interact. (TOCHI) 22(6), 32 (2015)

    Article  Google Scholar 

  46. Wijesekera, P., Baokar, A., Hosseini, A., Egelman, S., Wagner, D., Beznosov, K.: Android permissions remystified: a field study on contextual integrity. In: 24th \(\{\)USENIX\(\}\) Security Symposium (\(\{\)USENIX\(\}\) Security 15), pp. 499–514 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sven Bock .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bock, S., Momen, N. (2020). A Study on User Preference: Influencing App Selection Decision with Privacy Indicator. In: Stephanidis, C., Marcus, A., Rosenzweig, E., Rau, PL.P., Moallem, A., Rauterberg, M. (eds) HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies. HCII 2020. Lecture Notes in Computer Science(), vol 12423. Springer, Cham. https://doi.org/10.1007/978-3-030-60114-0_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60114-0_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60113-3

  • Online ISBN: 978-3-030-60114-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics