A dummy-based user privacy protection approach for text information retrieval

Text retrieval enables people to efficiently obtain the desired data from massive text data, so has become one of the most popular services in information retrieval community. However, while providing great convenience for users, text retrieval results in a serious issue on user privacy. In this paper, we propose a dummy-based approach for text retrieval privacy protection. Its basic idea is to use well-designed dummy queries to cover up user queries and thus protect user privacy. First, we present a client-based system framework for the protection of user privacy, which requires no change to the existing algorithm of text retrieval, and no compromise to the accuracy of text retrieval. Second, we define a user privacy model to formulate the requirements that ideal dummy queries should meet, i.e., (1) having highly similar feature distributions with user queries, and (2) effectively reducing the significance of user query topics. Third, by means of the knowledge derived from Wikipedia, we present an implementation algorithm to construct a group of ideal dummy queries that can well meet the privacy model. Finally, we demonstrate the effectiveness of our approach by theoretical analysis and experimental evaluation. The results show that by constructing dummy queries that have similar feature distributions but unrelated topics with user queries, the privacy behind users' textual queries can be effectively protected, under the precondition of not compromising the accuracy and usability of text retrieval. (C) 2020 Published by Elsevier B.V.