Query Obfuscation by Semantic Decomposition

Danushka Bollegala, Tomoya Machide, Ken-ichi Kawarabayashi

Abstract
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.
Anthology ID:
2022.lrec-1.667
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6200–6211
Language:
URL:
https://aclanthology.org/2022.lrec-1.667
DOI:
Bibkey:
Cite (ACL):
Danushka Bollegala, Tomoya Machide, and Ken-ichi Kawarabayashi. 2022. Query Obfuscation by Semantic Decomposition. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6200–6211, Marseille, France. European Language Resources Association.
Cite (Informal):
Query Obfuscation by Semantic Decomposition (Bollegala et al., LREC 2022)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.667.pdf
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