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.
Highlights
Web search engines (WSEs) have become an essential tool to find topic-specific information due to exponential growth in information and communication technology
Experiments are 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. e results show that the QuPiD Attack’s performance with Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM) is far better with query strings or textual data than the Topic Score in terms of Precision and Recall. e model formed with BiLSTM gave 0.512 Recall with the Precision of 0.923, whereas IBk gave 0.49 Recall with the Precision of 0.934 with the same data. e results show that the QuPiD Attack performance can be improved further using Artificial Neural Network techniques with fine-tuned parameters
0.225 0.332 0.481 considered user queries to build the prediction model instead of the Topic Score to study a model’s performance with query strings. e results show that the QuPiD Attack’s performance with LSTM and BiLSTM is far better with query strings or textual data compared to the Topic Score in terms of Precision and Recall. e model built with BiLSTM gave 0.512 Recall with the accuracy of 0.923 at 100 epochs, and LSTM gave 0.47 Recall with the Precision of 0.932 at 150 epochs
Summary
Web search engines (WSEs) have become an essential tool to find topic-specific information due to exponential growth in information and communication technology. The terms and conditions are vague, and the user profile may be exposed and misused by third parties, leading to serious privacy concerns [3]. Such an incident happened in 2007 when America Online (AOL) published the web search log of users [4] and in 2005 when the department of justice asked Google to submit their web search log [5]. Ese techniques include query scrambling [6], profile obfuscation [7], proxy services [8], and Private Information Retrieval (PIR) protocols [9,10,11,12]. Some studies indicate that PIR protocols are vulnerable to machine learning attacks [13, 14], especially QuPiD Attack [1, 3]
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