NN-QuPiD Attack: Neural Network-Based Privacy Quantification Model for Private Information Retrieval Protocols
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6651662 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850211066001227776 |
|---|---|
| author | Rafiullah Khan Mohib Ullah Atif Khan Muhammad Irfan Uddin Maha Al-Yahya |
| author_facet | Rafiullah Khan Mohib Ullah Atif Khan Muhammad Irfan Uddin Maha Al-Yahya |
| author_sort | Rafiullah Khan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-897b3b43fce7466693f276a72a8a08c1 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-897b3b43fce7466693f276a72a8a08c12025-08-20T02:09:38ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66516626651662NN-QuPiD Attack: Neural Network-Based Privacy Quantification Model for Private Information Retrieval ProtocolsRafiullah Khan0Mohib Ullah1Atif Khan2Muhammad Irfan Uddin3Maha Al-Yahya4Institute of Computer Science & Information Technology, The University of Agriculture, Peshawar, PakistanInstitute of Computer Science & Information Technology, The University of Agriculture, Peshawar, PakistanDepartment of Computer Science, Islamia College Peshawar, Peshawar, KP, PakistanInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanDepartment of Information Technology, College of Computer and Information Sciences, King Saud University, P. O. Box 145111, 4545 Riyadh, Saudi ArabiaWeb 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.http://dx.doi.org/10.1155/2021/6651662 |
| spellingShingle | Rafiullah Khan Mohib Ullah Atif Khan Muhammad Irfan Uddin Maha Al-Yahya NN-QuPiD Attack: Neural Network-Based Privacy Quantification Model for Private Information Retrieval Protocols Complexity |
| title | NN-QuPiD Attack: Neural Network-Based Privacy Quantification Model for Private Information Retrieval Protocols |
| title_full | NN-QuPiD Attack: Neural Network-Based Privacy Quantification Model for Private Information Retrieval Protocols |
| title_fullStr | NN-QuPiD Attack: Neural Network-Based Privacy Quantification Model for Private Information Retrieval Protocols |
| title_full_unstemmed | NN-QuPiD Attack: Neural Network-Based Privacy Quantification Model for Private Information Retrieval Protocols |
| title_short | NN-QuPiD Attack: Neural Network-Based Privacy Quantification Model for Private Information Retrieval Protocols |
| title_sort | nn qupid attack neural network based privacy quantification model for private information retrieval protocols |
| url | http://dx.doi.org/10.1155/2021/6651662 |
| work_keys_str_mv | AT rafiullahkhan nnqupidattackneuralnetworkbasedprivacyquantificationmodelforprivateinformationretrievalprotocols AT mohibullah nnqupidattackneuralnetworkbasedprivacyquantificationmodelforprivateinformationretrievalprotocols AT atifkhan nnqupidattackneuralnetworkbasedprivacyquantificationmodelforprivateinformationretrievalprotocols AT muhammadirfanuddin nnqupidattackneuralnetworkbasedprivacyquantificationmodelforprivateinformationretrievalprotocols AT mahaalyahya nnqupidattackneuralnetworkbasedprivacyquantificationmodelforprivateinformationretrievalprotocols |