Plausible Deniability in Web Search -- From Detection to Assessment
This addresses privacy concerns for web search users regarding sensitive topics, offering a practical tool and defense method.
The paper tackles the problem of defending users' ability to plausibly deny interest in sensitive topics against search engine learning, developing a tool called PDE that detects threats to plausible deniability and showing these threats are readily detectable for all tested topics, particularly in health and sexual preferences. They design a defense using proxy topics that proves more effective in experiments.
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.