  Our ability to control the flow of sensitive personal information to online
systems is key to trust in personal privacy on the internet. We ask how to
detect, assess and defend user privacy in the face of search engine
personalisation? We develop practical and scalable tools allowing a user to
detect, assess and defend against 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. We show this remains the
case when attempting to disrupt search engine learning through noise query
injection and click obfuscation are used. We use our model we design a defence
technique exploiting uninteresting, proxy topics and show that it provides
amore effective defence of plausible deniability in our experiments.
