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)


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.


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.


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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

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