Tomoya Machide


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A Neighbourhood-Aware Differential Privacy Mechanism for Static Word Embeddings
Danushka Bollegala | Shuichi Otake | Tomoya Machide | Ken-ichi Kawarabayashi
Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)


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Query Obfuscation by Semantic Decomposition
Danushka Bollegala | Tomoya Machide | Ken-ichi Kawarabayashi
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We propose a method to protect the privacy of search engine users by decomposing the queries using semantically related and unrelated 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 obfuscity achieved through the proposed query anonymisation method and the 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.