  Online advertising is progressively moving towards a programmatic model in
which ads are matched to actual interests of individuals collected as they
browse the web. Letting the huge debate around privacy aside, a very important
question in this area, for which little is known, is: How much do advertisers
pay to reach an individual? In this study, we develop a first of its kind
methodology for computing exactly that -- the price paid for a web user by the
ad ecosystem -- and we do that in real time. Our approach is based on tapping
on the Real Time Bidding (RTB) protocol to collect cleartext and encrypted
prices for winning bids paid by advertisers in order to place targeted ads. Our
main technical contribution is a method for tallying winning bids even when
they are encrypted. We achieve this by training a model using as ground truth
prices obtained by running our own "probe" ad-campaigns. We design our
methodology through a browser extension and a back-end server that provides it
with fresh models for encrypted bids. We validate our methodology using a one
year long trace of 1600 mobile users and demonstrate that it can estimate a
user's advertising worth with more than 82% accuracy.
