  From buying books to finding the perfect partner, we share our most intimate
wants and needs with our favourite online systems. But how far should we accept
promises of privacy in the face of personal profiling? In particular we ask how
can we improve detection of sensitive topic profiling by online systems? We
propose a definition of privacy disclosure we call
{\epsilon}-indistinguishability from which we construct scalable, practical
tools to assess an adversaries learning potential. We demonstrate our results
using openly available resources, detecting a learning rate in excess of 98%
for a range of sensitive topics during our experiments.
