  Although digital advertising fuels much of today's free Web, it typically
does so at the cost of online users' privacy, due to the continuous tracking
and leakage of users' personal data. In search for new ways to optimize the
effectiveness of ads, advertisers have introduced new advanced paradigms such
as cross-device tracking (CDT), to monitor users' browsing on multiple devices
and screens, and deliver (re)targeted ads in the most appropriate
screen.Unfortunately, this practice leads to greater privacy concerns for the
end-user. Going beyond the state-of-the-art, we propose a novel methodology for
detecting CDT and measuring the factors affecting its performance, in a
repeatable and systematic way. This new methodology is based on emulating
realistic browsing activity of end-users, from different devices, and thus
triggering and detecting cross-device targeted ads. We design and build Talon a
CDT measurement framework that implements our methodology and allows
experimentation with multiple parallel devices, experimental setups and
settings. By employing Talon, we perform several critical experiments, and we
are able to not only detect and measure CDT with average AUC score of
0.78-0.96, but also to provide significant insights about the behavior of CDT
entities and the impact on users' privacy. In the hands of privacy researchers,
policy makers and end-users, Talon can be an invaluable tool for raising
awareness and increasing transparency on tracking practices used by the
ad-ecosystem.
