文献类型：期刊文献

英文题名：A dummy-based user privacy protection approach for text information retrieval

作者：Wu, Zongda[1,3];Shen, Shigen[1];Lian, Xinze[2];Su, Xinning[3];Chen, Enhong[4]

机构：[1]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Zhejiang, Peoples R China;[2]Wenzhou Univ, Oujiang Coll, Wenzhou 325035, Zhejiang, Peoples R China;[3]Nanjing Univ, Sch Informat Management, Nanjing 210023, Jiangsu, Peoples R China;[4]Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Anhui, Peoples R China

年份：2020

卷号：195

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:000523561500022)、、EI(收录号：20200908243029)、Scopus(收录号：2-s2.0-85080039693)、WOS

基金：We thank anonymous reviewers for their constructive comments. The work is supported by the National Social Science Foundation of China (19BTQ056).

语种：英文

外文关键词：Text retrieval; Privacy protection; Feature distribution; Topic significance

外文摘要：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.