Advertisement

An Investigation of the Factors that Predict an Internet User’s Perception of Anonymity on the Web

  • Shruti Devaraj
  • Myrtede AlfredEmail author
  • Kapil Chalil Madathil
  • Anand K. Gramopadhye
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9190)

Abstract

The growth of the Internet as a means of communication has sparked a need for researchers to investigate the issues surrounding different social behaviors associated with Internet use. Of particular interest is the importance of a user’s perception of anonymity. The independent variables for the study were demographic information, social networking habits and prior negative experience. The dependent variable for this study was perception of online anonymity. Data for this analysis were taken from the Pew Research Center’s Internet & American Life Project’s July 2013 Pew Internet Anonymity Survey. A binomial logistic regression analysis was performed to predict perception of anonymity on the Web. Results indicated that gender, income level, education level, social networking habits and compromised identity are significant in predicting one’s perception of anonymity on the web. Age and prior negative experience were not significant predictors. Differences in technological proficiency and access to the web are two factors believed to have contributed to these results, particularly those related to demographics. The findings from this research could be used to help target demographics with the education and support needed to protect their identity on the web. This study also offers insight about who are more likely to attempt to use the web anonymously and will help further identify the patterns of behavior associated with anonymous web use. This paper calls for further studies to analyze to what extent do the opinions and experiences of friends and relatives impact an individual’s perception of anonymity.

Keywords

Social Networking Site Internet User Proxy Server Internet Protocol Address Post High School 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Abhyankar, A.: Social Networking Sites. Samvad, SIBM (2011)Google Scholar
  2. Ackeman, M.S., Cranor, L.F., Reagle, J.: Privacy in E-Commerce: Examining User Scenarios and Privacy Preferences. In: Proceedings of 1st ACM Conference on Electronic Commerce, pp. 1–8 (1999)Google Scholar
  3. Bakshy, E, Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: WWW 2012 – Session: Information Diffusion in Social Networks (2012)Google Scholar
  4. Berendt, B.: Privacy In E-Commerce: Stated Preferences Vs Actual Behavior. Commun. ACM 48(4), 101–106 (2005)CrossRefGoogle Scholar
  5. Bimber, B.: Measuring the gender gap on the internet. Soc. Sci. Q. 81(3), 868–876 (2000)Google Scholar
  6. Boyd, D.M., Ellison, N.B.: Social networking sites: definition, history and scholarship. J. Comput. Mediated Inf. 13(1), 210–230 (2007)CrossRefGoogle Scholar
  7. Chalil Madathil, K., Greenstein, JUST, Nines, DAM, Juan, K., Gramopadhye, O.K.: An investigation of the informational needs of Ovarian cancer patients and their supporters. In: Proceedings of the Human Factors and Ergonomics Society’s International Annual Meeting, San Diego, CA, September 2013Google Scholar
  8. Chen, K., Rea, A.: Protecting personal information online: a survey of user privacy concerns and control techniques. J. Comput. Inf. Syst. 44(4), 85–92 (2004)Google Scholar
  9. Clark, J., Van Borscht, P.C., Adams, C.: Usability of anonymous web browsing: an examination of tor interfaces and deploy ability. In: Proceedings of the 3rd Symposium on Usable Privacy and Security, pp. 41–51. ACM, July 2007Google Scholar
  10. DiMaggio, P., Hargittai, E.: From the ‘digital divide’to ‘digital inequality’: studying internet use as penetration increases. Princeton University Center for Arts and Cultural Policy Studies, Working Paper Series number, 15 (2001)Google Scholar
  11. Fraune, M., Juang, K., Greenstein, J.S., Chalil Madathil, K., Koikkara, R.: Employing user-created pictures to enhance the recall of system-generated mnemonic phrases and security of Passwords. In: Proceedings of the Human Factors and Ergonomics Society’s International Annual Meeting, San Diego, CA (2013)Google Scholar
  12. Gefen, D., Straub, D.: Gender differences in the perception and use of e-mail: an acceptance of the technology acceptance model. MIS Q. /December 21, 389–400 (1997)CrossRefGoogle Scholar
  13. Hargittai, E., Shafer, S.: Differences In Actual And Perceived Online Skills: The Role Of Gender. Social Science Quarterly 87(2), 432–448 (2006)CrossRefGoogle Scholar
  14. Holt, D.T., Crocker, M.: Prior negative experiences: their impacts on computer training outcomes. Comput. Educ. 35(4), 295–308 (2000)CrossRefGoogle Scholar
  15. Protalinski, E.: Facebook: 8.7 percent are fake users [Web blog post] (2012). http://www.cnet.com
  16. Kang, R., Brown, S., Kiesler, S.: Why do people seek anonymity on the internet? informing policy and design. CHI 2013, April 27–May 2 2013Google Scholar
  17. Lehman, D.: Protecting kids’ privacy is costly, Computer World, April, p. 97 (2007)Google Scholar
  18. Lin, J.C., Lu, H.: Towards an understanding of the behavioral intention to use a web site. Int. J. Inf. Manage. 20(3), 197–208 (2000)CrossRefGoogle Scholar
  19. Marx, G.T.: what’s in a name? some reflections on the sociology of anonymity. Inf. Soc. 15(2), 99–112 (1999)CrossRefGoogle Scholar
  20. Porter, C.E., Donthu, N.: Using the technology acceptance model to explain how attitudes determine internet usage: the role of perceeived access barriers and demographics. J. Bus. Res. 59(9), 999–1007 (2006)CrossRefGoogle Scholar
  21. Rezgui, A., Bouguettaya, A., Eltoweissy, M.Y.: Privacy on the web: facts, challenges and solutions. IEEE Secur. Priv. 1(6), 40–49 (2003)CrossRefGoogle Scholar
  22. Rezgui, A., Ouzzani, M., Bouguettaya, A., Medjahed, B.: Preserving privacy in web services ACM 1-58113-593-9/02/001 (2002)Google Scholar
  23. Subrahmanyam, K., Reich, S.M., Waechter, N., Espinoza, G.: Online and offline social networks: use of social networking sites by emerging adults. J. Appl. Dev. Psychol. 29, 420–433 (2008)CrossRefGoogle Scholar
  24. Sweeney, J.C., Soutar, G.N., Mazzoral, T.: Factors influencing word of mouth effectiveness: receiver perspectives. Eur. J. Mark. 42(3/4), 344–364 (2008)CrossRefGoogle Scholar
  25. Traynor, M.: Anonymity and the internet. Comput. Internet Lawyer 22, 2 (2005)Google Scholar
  26. Turner, E., Dasgupta, S.: Privacy on the web: an examination of user concerns, technology and implications for business organizations and organization. Inf. Syst. Manage. 20(1), 8–19 (2003)CrossRefGoogle Scholar
  27. USC Annenberg School of Communication-Center for the Digital Future, Surveying the digital future-year four. http://www.digitalcenter.org/downloads/DigitalFutureReport-Year4-2004.pdf
  28. Wismer, A., Chalil Madathil, K., Koikkara, R., Juang, K., Greenstein, J.S.: Evaluating the usability of CAPTCHAs on a mobile device with voice and touch input. In: Proceedings of the Human Factors and Ergonomics Society 56th Annual Meeting, Boston, MA (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shruti Devaraj
    • 1
  • Myrtede Alfred
    • 1
    Email author
  • Kapil Chalil Madathil
    • 1
  • Anand K. Gramopadhye
    • 1
  1. 1.Clemson UniversityClemsonUSA

Personalised recommendations