Web Privacy Measurement: Early Results, Engineering Challenges and Selected Applications
Web Privacy Measurement experiments have heavily in
uenced privacy debates by
shedding light into practices such as third party online tracking and price discrimination.
However, these research experiments have typically been one-o projects,
with dierent groups encountering similar methodological and engineering challenges
without forming an institutional knowledge base. As an illustrative example of this
trend of repeating eort through self-contained experiments, we present the results
of one study comparing mobile and desktop tracking and discuss how challenges from
this study and other works in the literature shaped the formation of general design
principles intended to improve the eciency of such experiments.
We present a robust and modular web measurement platform that enables scalable
and repeatable experiments while avoiding many common pitfalls observed by the
research community. We describe case studies performed on this framework, including
the detection of unique cookie identiers, the detection of third-party synchronization
of these IDs and an examination into the personalization of the content on news sites
based on user history.