QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

Abstract

With the advancement in ICT, web search engines have become a preferred source to find health-related information published over the Internet. Google alone receives more than one billion health-related queries on a daily basis. However, in order to provide the results most relevant to the user, WSEs maintain the users’ profiles. These profiles may contain private and sensitive information such as the user’s health condition, disease status, and others. Health-related queries contain privacy-sensitive information that may infringe user’s privacy, as the identity of a user is exposed and may be misused by the WSE and third parties. This raises serious concerns since the identity of a user is exposed and may be misused by third parties. One well-known solution to preserve privacy involves issuing the queries via peer-to-peer private information retrieval protocol, such as useless user profile (UUP), thereby hiding the user’s identity from the WSE. This paper investigates the level of protection offered by UUP. For this purpose, we present QuPiD (query profile distance) attack: a machine learning-based attack that evaluates the effectiveness of UUP in privacy protection. QuPiD attack determines the distance between the user’s profile (web search history) and upcoming query using our proposed novel feature vector. The experiments were conducted using ten classification algorithms belonging to the tree-based, rule-based, lazy learner, metaheuristic, and Bayesian families for the sake of comparison. Furthermore, two subsets of an America Online dataset (noisy and clean datasets) were used for experimentation. The results show that the proposed QuPiD attack associates more than 70% queries to the correct user with a precision of over 72% for the clean dataset, while for the noisy dataset, the proposed QuPiD attack associates more than 40% queries to the correct user with 70% precision.

NN‐QuPiD Attack: Neural Network‐Based Privacy Quantification Model for Private Information Retrieval Protocols

“…Similarly, Basla et al [20] proposed a dummy queries classification, semantic classification, and profile filteringbased privacy evaluation model for profile obfuscationbased solutions. Khan et al proposed QuPiD (Query Profile Distance) attack [1,13] purely for the evaluation of the PIR protocols. QuPiD Attack uses a user's history and machine learning algorithm to build a prediction model that can associate the anonymized query with the correct user.…”

“…Web search engines (WSEs) have become an essential tool to find topic-specific information due to exponential growth in information and communication technology. To give the most relevant results to the user, WSE maintains his/her profile [1]. e user profile carries the user's web search queries; however, it may contain sensitive information about the user, such as health condition, gender, political affiliation, and religious affiliations [2].…”

“…In the proxy-based approach, the user submits his/her query to the WSE through the proxy server, whereas, in PIR protocols, a group of users submit queries on behalf of each other to hide their identity. According to the literature, PIR protocols provide better privacy to WSE users as compared to other techniques [1][2][3][4][5][6][7][8][9][10][11][12][13]. Some studies indicate that PIR protocols are vulnerable to machine learning attacks [13,14], especially QuPiD Attack [1,3].…”

“…Moreover, we used the Topic Score feature vector for training and testing of the QuPiD Attack. e Topic Score feature vector comprises a set of numeric values of 10 major topics acquired from the uClassify service [1][2][3][4][5][6][7][8][9][10][11][12][13]. According to the results, QuPiD Attack associated 40% anonymized queries with the correct user with 70% Precision.…”

“…e experiments are conducted with Multilayer Perceptron and Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM) classification algorithms with a benchmark dataset released by AOL. Moreover, experiments are also conducted with the IBk (Instance-Based K-Nearest Neighbours) classification algorithm, as IBk performed well in the previous QuPiD Attack version [1]. e experiments are conducted with the Topic Score and query string feature vectors.…”