  We ask how to defend user ability to plausibly deny their interest in topics
deemed sensitive in the face of search engine learning. We develop a practical
and scalable tool called \PDE{} allowing a user to detect and assess threats to
plausible deniability. We show that threats to plausible deniability of
interest are readily detectable for all topics tested in an extensive testing
program. Of particular concern is observation of threats to deniability of
interest in topics related to health and sexual preferences. We show this
remains the case when attempting to disrupt search engine learning through
noise query injection and click obfuscation. We design a defence technique
exploiting uninteresting, proxy topics and show that it provides a more
effective defence of plausible deniability in our experiments.
