Advertisement

Automated Measurements of Cross-Device Tracking

  • Konstantinos SolomosEmail author
  • Panagiotis Ilia
  • Sotiris Ioannidis
  • Nicolas Kourtellis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11398)

Abstract

Although digital advertising fuels much of today’s free Web, it typically do so at the cost of online users’ privacy, due to continuous tracking and leakage of users’ personal data. In search for new ways to optimize effectiveness of ads, advertisers have introduced new paradigms such as cross-device tracking (CDT), to monitor users’ browsing on multiple screens, and deliver (re)targeted ads in the appropriate screen. Unfortunately, this practice comes with even more privacy concerns for the end-user. In this work, we design a methodology for triggering CDT by emulating realistic browsing activity of end-users, and then detecting and measuring it by leveraging advanced machine learning tools.

Notes

Acknowledgments

This research has received funding from the European Union’s Horizon 2020 research and innovation programme under Grand Agreement No. 700378 (project CIPSEC) and the Marie Sklodowska-Curie Grand Agreement No. 690972 (project PROTASIS). This paper reflects only the authors’ view and the Agency is not responsible for any use that may be made of the information it contains.

References

  1. 1.
    FTC: Cross-device tracking. Technical report (2017)Google Scholar
  2. 2.
    Mavroudis, V., Hao, S., Fratantonio, Y., Maggi, F., Kruegel, C., Vigna, G.: On the privacy and security of the ultrasound ecosystem. Proc. Priv. Enhancing Technol. 2017(2), 95–112 (2017)CrossRefGoogle Scholar
  3. 3.
    Arp, D., Quiring, E., Wressnegger, C., Rieck, K.: Privacy threats through ultrasonic side channels on mobile devices. In: 2017 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 35–47. IEEE (2017)Google Scholar
  4. 4.
    Brookman, J., Rouge, P., Alva, A., Yeung, C.: Cross-device tracking: measurement and disclosures. Proc. Priv. Enhancing Technol. 2017(2), 134–149 (2017)Google Scholar
  5. 5.
    Zimmeck, S., Li, J.S., Kim, H., Bellovin, S.M., Jebara, T.: A privacy analysis of cross-device tracking. In: 26th USENIX Security Symposium, USENIX Security 2017, Vancouver, BC, pp. 1391–1408. USENIX Association (2017)Google Scholar
  6. 6.
    Carrascosa, J.M., Mikians, J., Cuevas, R., Erramilli, V., Laoutaris, N.: I always feel like somebody’s watching me: measuring online behavioural advertising. In: Proceedings of the 11th ACM Conference on Emerging Networking Experiments and Technologies, CoNEXT 2015, pp. 13:1–13:13. ACM, New York (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Konstantinos Solomos
    • 1
    Email author
  • Panagiotis Ilia
    • 1
  • Sotiris Ioannidis
    • 1
  • Nicolas Kourtellis
    • 2
  1. 1.FORTHHeraklionGreece
  2. 2.Telefonica ResearchBarcelonaSpain

Personalised recommendations