  Today's Web services - such as Google, Amazon, and Facebook - leverage user
data for varied purposes, including personalizing recommendations, targeting
advertisements, and adjusting prices. At present, users have little insight
into how their data is being used. Hence, they cannot make informed choices
about the services they choose. To increase transparency, we developed XRay,
the first fine-grained, robust, and scalable personal data tracking system for
the Web. XRay predicts which data in an arbitrary Web account (such as emails,
searches, or viewed products) is being used to target which outputs (such as
ads, recommended products, or prices). XRay's core functions are service
agnostic and easy to instantiate for new services, and they can track data
within and across services. To make predictions independent of the audited
service, XRay relies on the following insight: by comparing outputs from
different accounts with similar, but not identical, subsets of data, one can
pinpoint targeting through correlation. We show both theoretically, and through
experiments on Gmail, Amazon, and YouTube, that XRay achieves high precision
and recall by correlating data from a surprisingly small number of extra
accounts.
