TY - JOUR
T1 - NN-QuPiD Attack
T2 - neural network-based privacy quantification model for private information retrieval protocols
AU - Khan, Rafiullah
AU - Ullah, Mohib
AU - Khan, Atif
AU - Uddin, Muhammad Irfan
AU - Al-Yahya, Maha
N1 - 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.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85101052643&partnerID=8YFLogxK
U2 - 10.1155/2021/6651662
DO - 10.1155/2021/6651662
M3 - Article
SN - 1076-2787
VL - 2021
SP - 1
EP - 8
JO - Complexity
JF - Complexity
M1 - 6651662
ER -