NN-QuPiD Attack: neural network-based privacy quantification model for private information retrieval protocols

Rafiullah Khan*, Mohib Ullah, Atif Khan, Muhammad Irfan Uddin, Maha Al-Yahya

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
50 Downloads (Pure)

Abstract

Web search engines usually keep users' 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' privacy since a user's 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-QuPiD 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-QuPiD 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.

Original languageEnglish
Article number6651662
Pages (from-to)1-8
Number of pages8
JournalComplexity
Volume2021
DOIs
Publication statusPublished - 2021
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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