{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T20:32:00Z","timestamp":1759091520435,"version":"3.41.2"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"vor","delay-in-days":32,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["RSP-2020\/286"],"award-info":[{"award-number":["RSP-2020\/286"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Web search engines usually keep users\u2019 profiles for multiple purposes, such as result ranking and relevancy, market research, and targeted advertisements. However, user web search history may contain sensitive and private information about the user, such as health condition, personal interests, and affiliations that may infringe users\u2019 privacy since a user\u2019s identity may be exposed and misused by third parties. Numerous techniques are available to address privacy infringement, including Private Information Retrieval (PIR) protocols that use peer nodes to preserve privacy. Previously, we have proved that PIR protocols are vulnerable to the QuPiD Attack. In this research, we proposed NN\u2010QuPiD Attack, an improved version of QuPiD Attack that uses an Artificial Neural Network (RNN) based model to associate queries with their original users. The results show that the NN\u2010QuPiD Attack gave 0.512 Recall with the Precision of 0.923, whereas simple QuPiD Attack gave 0.49 Recall with the Precision of 0.934 with the same data.<\/jats:p>","DOI":"10.1155\/2021\/6651662","type":"journal-article","created":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T21:35:34Z","timestamp":1612301734000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["NN\u2010QuPiD Attack: Neural Network\u2010Based Privacy Quantification Model for Private Information Retrieval Protocols"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0229-7747","authenticated-orcid":false,"given":"Rafiullah","family":"Khan","sequence":"first","affiliation":[]},{"given":"Mohib","family":"Ullah","sequence":"additional","affiliation":[]},{"given":"Atif","family":"Khan","sequence":"additional","affiliation":[]},{"given":"Muhammad Irfan","family":"Uddin","sequence":"additional","affiliation":[]},{"given":"Maha","family":"Al-Yahya","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8868686"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1166\/jmihi.2019.2709"},{"key":"e_1_2_10_3_2","unstructured":"KhanR. On the effectiveness of private information retrieval protocols 2020 Department of Computer Science Capital University of Science and Technology Islamabad Pakistan Ph.D. dissertation."},{"key":"e_1_2_10_4_2","unstructured":"AdarE. User 4xxxxx9: anonymizing query logs Proceedings of Query Log Analysis Workshop International Conference on World Wide Web May 2007 Alberta Canada."},{"key":"e_1_2_10_5_2","unstructured":"PetitA. Introducing privacy in current web search engines 2017 Universit\u00e9 de Lyon France Ph.D dissertation."},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-015-9256-0"},{"key":"e_1_2_10_7_2","unstructured":"ToubianaV. SubramanianL. andNissenbaumH. Trackmenot: enhancing the privacy of web search 2011 arXiv:1109.4677."},{"key":"e_1_2_10_8_2","doi-asserted-by":"crossref","unstructured":"MokhtarS. B. BoutetA. FelberP. PasinM. PiresR. andSchiavoniV. 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