Query Obfuscation Semantic Decomposition
This addresses privacy concerns for search engine users by obfuscating queries, though it is an incremental improvement on existing anonymization techniques.
The paper tackles the problem of protecting search engine users' privacy by decomposing queries into semantically related and unrelated distractor terms, then reconstructing search results from these decomposed queries. It shows that the method accurately reconstructs search results without compromising user privacy, as evaluated empirically against clustering-based attacks.
We propose a method to protect the privacy of search engine users by decomposing the queries using semantically \emph{related} and unrelated \emph{distractor} terms. Instead of a single query, the search engine receives multiple decomposed query terms. Next, we reconstruct the search results relevant to the original query term by aggregating the search results retrieved for the decomposed query terms. We show that the word embeddings learnt using a distributed representation learning method can be used to find semantically related and distractor query terms. We derive the relationship between the \emph{obfuscity} achieved through the proposed query anonymisation method and the \emph{reconstructability} of the original search results using the decomposed queries. We analytically study the risk of discovering the search engine users' information intents under the proposed query obfuscation method, and empirically evaluate its robustness against clustering-based attacks. Our experimental results show that the proposed method can accurately reconstruct the search results for user queries, without compromising the privacy of the search engine users.