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.
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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.
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Solomos, K., Ilia, P., Ioannidis, S., Kourtellis, N. (2019). Automated Measurements of Cross-Device Tracking. In: Fournaris, A., Lampropoulos, K., Marín Tordera, E. (eds) Information and Operational Technology Security Systems. IOSec 2018. Lecture Notes in Computer Science(), vol 11398. Springer, Cham. https://doi.org/10.1007/978-3-030-12085-6_7
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DOI: https://doi.org/10.1007/978-3-030-12085-6_7
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