  Numerous surveys have shown that Web users are concerned about the loss of
privacy associated with online tracking. Alarmingly, these surveys also reveal
that people are also unaware of the amount of data sharing that occurs between
ad exchanges, and thus underestimate the privacy risks associated with online
tracking.
  In reality, the modern ad ecosystem is fueled by a flow of user data between
trackers and ad exchanges. Although recent work has shown that ad exchanges
routinely perform cookie matching with other exchanges, these studies are based
on brittle heuristics that cannot detect all forms of information sharing,
especially under adversarial conditions.
  In this study, we develop a methodology that is able to detect client- and
server-side flows of information between arbitrary ad exchanges. Our key
insight is to leverage retargeted ads as a tool for identifying information
flows. Intuitively, our methodology works because it relies on the semantics of
how exchanges serve ads, rather than focusing on specific cookie matching
mechanisms. Using crawled data on 35,448 ad impressions, we show that our
methodology can successfully categorize four different kinds of information
sharing behavior between ad exchanges, including cases where existing heuristic
methods fail.
  We conclude with a discussion of how our findings and methodologies can be
leveraged to give users more control over what kind of ads they see and how
their information is shared between ad exchanges.
