  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 features 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 experiment 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.
