DOI QR코드

DOI QR Code

How do multilevel privacy controls affect utility-privacy trade-offs when used in mobile applications?

  • Kim, Seung-Hyun (Hyper-connected Communication Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Ko, In-Young (School of Computing, Korea Advanced Institute of Science and Technology)
  • 투고 : 2017.11.06
  • 심사 : 2018.08.01
  • 발행 : 2018.12.06

초록

In existing mobile computing environments, users need to choose between their privacy and the services that they can receive from an application. However, existing mobile platforms do not allow users to perform such trade-offs in a fine-grained manner. In this study, we investigate whether users can effectively make utility-privacy trade-offs when they are provided with a multilevel privacy control method that allows them to recognize the different quality of service that they will receive from an application by limiting the disclosure of their private information in multiple levels. We designed a research model to observe users' utility-privacy trade-offs in accordance with the privacy control methods and other factors such as the trustworthiness of an application, quality level of private information, and users' privacy preferences. We conducted a user survey with 516 participants and found that, compared with the existing binary privacy controls, both the service utility and the privacy protection levels were significantly increased when the users used the multilevel privacy control method.

키워드

참고문헌

  1. M. Hartmann, Mobile privacy: contexts, privacy, online, Springer, Berlin, Heidelberg, 2011, pp. 191-203.
  2. T. Dinev and P. Hart, An extended privacy calculus model for ecommerce transactions, Inform. Syst. Res. 17 (2006), no. 1, 61-80.
  3. H. Xu et al., The personalization privacy paradox: an exploratory study of decision making process for location-aware marketing, Decision Supp. Syst. 51 (2011), no. 1, 42-52. https://doi.org/10.1016/j.dss.2010.11.017
  4. T. Li and T. Unger, Willing to pay for quality personalization? Trade-off between quality and privacy, Eur. J. Inform. Syst. 21 (2012), no. 6, 621-642. https://doi.org/10.1057/ejis.2012.13
  5. S. Petronio, Boundaries of privacy: dialectics of disclosure, State University of New York Press, Albany, NY, 2002.
  6. M. J. Keith et al., Privacy assurance and network effects in the adoption of location-based services: an iphone experiment, Proc. Int. Conf. Inform. Syst., St. Louis, MO, USA, 2010, pp. 237-255.
  7. T. Dinev et al., Information privacy and correlates: an empirical attempt to bridge and distinguish privacy-related concepts, Eur. J. Inform. Syst. 22 (2013), no. 3, 295-316. https://doi.org/10.1057/ejis.2012.23
  8. S. H. Kim et al., Effects of contextual properties on users' privacy preferences in mobile computing environments, IEEE Trustcom/ BigDataSE/ISPA, Helsinki, Finland, Aug. 20-22, 2015, pp. 507-514.
  9. G. R. Milne and M. E. Boza, Trust and concern in consumers' perceptions of marketing information management practices, J. Interact. Market. 13 (1999), no. 1, 5-24. https://doi.org/10.1002/(SICI)1520-6653(199924)13:1<5::AID-DIR2>3.0.CO;2-9
  10. R. K. Chellappa and R. G. Sin, Personalization versus privacy: an empirical examination of the online consumer's dilemma, Inform. Technol. Manag. 6 (2005), no. 2-3, 181-202. https://doi.org/10.1007/s10799-005-5879-y
  11. S. Y. Komiak and I. Benbasat, The effects of personalization and familiarity on trust and adoption of recommendation agents, MIS Quarterly 30 (2006), no. 4, 941-960. https://doi.org/10.2307/25148760
  12. F. Kehr et al., Blissfully ignorant: the effects of general privacy concerns, general institutional trust, and affect in the privacy calculus, Inform. Syst. J. 25 (2015), no. 6, 607-635. https://doi.org/10.1111/isj.12062
  13. H. Li, R. Sarathy, and H. Xu, The Role of affect and cognition on online consumers' decision to disclose personal information to unfamiliar online vendors, Decision Supp. Syst. 51 (2011), no. 3, 434-445. https://doi.org/10.1016/j.dss.2011.01.017
  14. N. K. Malhotra, S. S. Kim, and J. Agarwal, Internet users' information privacy concerns (IUIPC): the construct, the scale, and a causal model, Inform. Syst. Res. 15 (2004), no. 4, 336-355. https://doi.org/10.1287/isre.1040.0032
  15. M. Lwin, J. Wirtz, and J. D. Williams, Consumer online privacy concerns and responses: a power-responsibility equilibrium perspective, J. Acad. Market. Sci. 35 (2007), no. 4, 572-585. https://doi.org/10.1007/s11747-006-0003-3
  16. J. Omarzu, A disclosure decision model: determining how and when individuals will self-disclose, Pers. Soc. Psychol. Rev. 4 (2000), no. 2, 174-185. https://doi.org/10.1207/S15327957PSPR0402_05
  17. B. P. Knijnenburg and A. Kobsa, Making decisions about privacy: information disclosure in context-aware recommender systems, ACM Trans. Interaction Intell. Syst. 3 (2013), no. 3, 20-52.
  18. B. P. Knijnenburg and A. Kobsa, Helping users with information disclosure decisions: potential for adaptation, Proc. Int. Conf. Intell. User Interf., Santa Monica, CA, USA, Mar. 19-22, 2013, pp. 407-416.
  19. F. Kehr et al., Rethinking privacy decisions: pre-existing attitudes, pre-existing emotional states, and a situational privacy calculus, ECIS Proc., Greece, 2015, pp. 1-15.
  20. B. Zhang, N. Wang, and H. Jin, Privacy concerns in online recommender systems: influences of control and user data input, Proc. Symp. Usable Privacy Secur., Menlo Park, CA, USA, July 9-11, 2014, pp. 159-173.
  21. A. Berezowska et al., Consumer adoption of personalised nutrition services from the perspective of a risk-benefit trade-off, Genes Nutrition 10 (2015), no. 6, 1-16. https://doi.org/10.1007/s12263-014-0449-8
  22. B. P. Knijnenburg, A. Kobsa, and H. Jin, Preference-based location sharing: are more privacy options really better?, Proc. SIGCHI Conf. Human Factors Comput. Syst., Paris, France, Apr. 27-May 2, 2013, pp. 2667-2676.
  23. K. Haslum, A. Abraham, and S. Knapskog, Fuzzy online risk assessment for distributed intrusion prediction and prevention systems, Int. Conf. Comput. Modeling Simulation, Cambridge, UK, Apr. 1-3, 2008, pp. 216-223.
  24. Wikipedia. Margin of error, available at http://en.wikipedia.org/ wiki/Margin_of_error (Apr. 18, 2018).
  25. C. M. Ringle, S. Wende, and J. M. Becker, SmartPLS 3. Boenningstedt: SmartPLS GmbH, 2015, available at http://www.smart pls.com (Oct. 30, 2017).
  26. M. J. Keith et al., Information disclosure on mobile devices: reexamining privacy calculus with actual user behavior, Int. J. Human-Comput. Studies 71 (2013), no. 12, 1163-1173. https://doi.org/10.1016/j.ijhcs.2013.08.016
  27. J. Cohen, Statistical power analysis for the behavioral sciences, Lawrence Erlbaum, Hillsdale, NJ, 1988.
  28. M. Tenenhaus et al., PLS path modeling, Comput. Stat. Data Anal. 48 (2005), no. 1, 159-205. https://doi.org/10.1016/j.csda.2004.03.005
  29. D. Temme, H. Kreis, and L. Hildebrandt, PLS path modeling-a software review, SFB 649 Discussion Paper 2006-084, Institute of Marketing, Berlin, Germany, 2006.