06/2018 Journalbeiträge

Haupt, Johannes | Bender, Benedict | Fabian, Benjamin | Lessmann, Stefan

Robust identification of email tracking: A machine learning approach


Email tracking allows email senders to collect fine-grained behavior and location data on email recipients, who are uniquely identifiable via their email address. Such tracking invades user privacy in that email tracking techniques gather data without user consent or awareness. Striving to increase privacy in email communication, this paper develops a detection engine to be the core of a selective tracking blocking mechanism in the form of three contributions. First, a large collection of email newsletters is analyzed to show the wide usage of tracking over different countries, industries and time. Second, we propose a set of features geared towards the identification of tracking images under real-world conditions. Novel fea- tures are devised to be computationally feasible and efficient, generalizable and resilient towards changes in tracking infrastructure. Third, we test the predictive power of these features in a benchmarking exper- iment using a selection of state-of-the-art classifiers to clarify the effectiveness of model-based tracking identification. We evaluate the expected accuracy of the approach on out-of-sample data, over increasing periods of time, and when faced with unknown senders.

Kategorie Journalbeiträge
Autoren Haupt, Johannes; Bender, Benedict; Fabian, Benjamin; Lessmann, Stefan
Zeitschrift European Journal of Operations Research (EJOR)
Datum 06/2018
Volume 2018
Ausgabe 271
pp. 341-356
Verlag Elsevier
DOI https://doi.org/10.1016/j.ejor.2018.05.018
Keywords Analytics, Data privacy, Email tracking, Machine learning
ISSN 0377-2217
BibTex @artice{article,author = {Haupt, Johannes and Bender, Benedict and Fabian, Benjamin and Lessmann, Stefan}, year = {2018}, month = {6}, pages = {341-356}, title = {Robust identification of email tracking: A machine learning approach}, journal = {European Journal of Operations Research (EJOR)}, doi = {https://doi.org/10.1016/j.ejor.2018.05.018}, keywords = {Analytics, Data privacy, Email tracking, Machine learning}}