Advertisement

Improving the Robustness of the Cross-Domain Tracking Process

  • Bede Ravindra AmarasekaraEmail author
  • Anuradha Mathrani
  • Chris Scogings
Conference paper
  • 237 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1178)

Abstract

HTTP-cookie based tracking technologies provide efficient and reliable tracking capabilities across web domains on the internet. Different entities track user activity for various purposes. E-commerce practitioners need a reliable tracking system to quantify and reward visitor traffic generators. Business analytic providers track user interactions to generate customer behavioral insight that assist targeted marketing capabilities. Governments and security agencies track user activity to prevent national security threats. In a previous research study, the authors uncovered instances when tracking efforts can fail due to technical limitations, and instances where tracking results can be skewed fraudulently for monetary gain. In this research, we have examined additional tracking techniques that can be combined with the existing tracking methods to improve the robustness of the cookie-based tracking process. Following design science methodology, a domain-based network environment setup with bespoke web server software has been used to investigate the usability and robustness of the proposed tracking techniques.

Keywords

Cross-domain Tracking Affiliate marketing HTTP cookie XDT 

References

  1. 1.
    Kristol, D.M.: HTTP State Management Mechanism (RFC 2109). Internet RFCs 2109 (1997)Google Scholar
  2. 2.
    Amarasekara, B.R., Mathrani, A.: Exploring risk and fraud scenarios in affiliate marketing technologies from the advertiser’s perspective. In: Australasian Conference in Information Systems 2015, Adelaide (2015)Google Scholar
  3. 3.
    Chachra, N., Savage, S., Voelker, G.M.: Affiliate crookies: characterizing affiliate marketing abuse. In: IMC 2015 Proceedings of the 2015 ACM Conference on Internet Measurement Conference, pp. 41–47. ACM, New York (2015)Google Scholar
  4. 4.
    Edelman, B., Brandi, W.: Risk, information, and incentives in online affiliate marketing. J. Market. Res. LII, 1–12 (2015)CrossRefGoogle Scholar
  5. 5.
    W3C: W3C Recommendation - Web Storage (2013). 20130730. https://www.w3.org/TR/2013/REC-webstorage-20130730/
  6. 6.
    Englehardt, S., Narayanan, A.: Online tracking: a 1-million-site measurement and analysis. In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security. Association for Computing Machinery (2016)Google Scholar
  7. 7.
    Laperdrix, P., Rudametkin, W., Baudry, B.: Beauty and the beast: diverting modern web browsers to build unique browser fingerprints. In: 37th IEEE Symposium on Security and Privacy, San Jose (2016)Google Scholar
  8. 8.
    Fielding, R., Reschke, J.: Hypertext Transfer Protocol (HTTP/1.1): Conditional Requests. Internet RFCs RFC 7232 (2014)Google Scholar
  9. 9.
    Ayenson, M.D., Wambach, D.J., Soltani, A., Good, N., Hoofnagle, C.J.: Flash cookies and privacy II: now with HTML5 and ETag respawning. In: World Wide Web Internet and Web Information Systems (2011)Google Scholar
  10. 10.
    Soltani, A., Canty, S., Mayo, Q., Thomas, L., Hoofnagle, C.J.: Flash cookies and privacy. In: AAAI Spring Symposium: Intelligent Information Privacy Management, pp. 158–163 (2010)Google Scholar
  11. 11.
    Adobe: Adobe Flash Player - Local Settings Manager (2015). https://help.adobe.com/archive/en_US/FlashPlayer/LSM/flp_local_settings_manager.pdf
  12. 12.
    Brear, D., Barnes, S.J.: Assessing the value of online affiliate marketing in the UK financial services industy. Int. J. Electron. Finance (2008)Google Scholar
  13. 13.
    Norouzi, A.: An integrated survey in affiliate marketing network. In: Press Academia Procedia, pp. 299–309 (2017)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Libert, T.: Exposing the invisible web: an analysis of third-party HTTP requests on 1 million websites. Int. J. Commun. (2015)Google Scholar
  15. 15.
    Hoofnagle, C.J., Urban, J., Li, S.: Privacy and modern advertising: most us internet users want ‘Do Not Track’ to stop collection of data about their online activities. In: Amsterdam Privacy Conference (2012)Google Scholar
  16. 16.
    Baumann, A., Haupt, J., Gebert, F., Lessmann, S.: The price of privacy: an evaluation of the economic value of collecting clickstream data. Bus. Inf. Syst. Eng., 1–19 (2018)Google Scholar
  17. 17.
    Richterich, A.: How data-driven research fuelled the Cambridge Analytica controversy. Open J. Sociopolitical Stud. 11(2), 528–543 (2018)Google Scholar
  18. 18.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28(1), 75–105 (2004)CrossRefGoogle Scholar
  19. 19.
    March, S.T., Smith, G.: Design and natural science research on information technology. Decis. Support Syst. 15(4), 251–266 (1995)CrossRefGoogle Scholar
  20. 20.
    Nunamaker, J., Chen, M., Pruding, T.D.M.: Systems development in information systems research. J. Manag. Inf. Syst. 7(3), 89–106 (1991)CrossRefGoogle Scholar
  21. 21.
    Eckersley, P.: How unique is your web browser? In: Atallah, M.J., Hopper, N.J. (eds.) PETS 2010. LNCS, vol. 6205, pp. 1–18. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-14527-8_1CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Natural and Computational SciencesMassey UniversityAlbanyNew Zealand

Personalised recommendations