文献类型：期刊文献

英文题名：A Basic Framework for Privacy Protection in Personalized Information Retrieval: An Effective Framework for User Privacy Protection

作者：Wu, Zongda[1];Shen, Shigen[2];Li, Huxiong[1];Zhou, Haiping[1];Lu, Chenglang[3]

机构：[1]Shaoxing Univ, Comp Sci, Shaoxing, Peoples R China;[2]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing, Peoples R China;[3]Zhejiang Inst Mech & Elect Engn, Comp Sci, Hangzhou, Peoples R China

年份：2021

卷号：33

期号：6

外文期刊名：JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING

收录：SSCI(收录号：WOS:000880113900001)、SCI-EXPANDED(收录号：WOS:000880113900001)、、WOS

基金：The work is supported by the key project of Humanities and Social Sciences in Colleges and Universities of Zhejiang Province (No 2021GH017), Humanities and Social Sciences Project of the Ministry of Education of China (Nos 21YJA870011 and 21YJCZH096), Zhejiang Philosophy and Social Science Planning Project (No 22ZJQN45YB) and National Social Science Foundation of China (No 21FTQB019)

语种：英文

外文关键词：Algorithm; Constraint; Framework; Information Retrieval; Personalized; Privacy Model; User Privacy

外文摘要：Personalized information retrieval is an effective tool to solve the problem of information overload. Along with the rapid development of emerging network technologies such as cloud computing, however, network servers are becoming more and more untrusted, resulting in a serious threat to user privacy of personalized information retrieval. In this paper, the authors propose a basic framework for the comprehensive protection of all kinds of user privacy in personalized information retrieval. Its basic idea is to construct and submit a group of well-designed dummy requests together with each user request to the server to mix up the user requests and then cover up the user privacy behind the requests. Also, the framework includes a privacy model and its implementation algorithm. Finally, theoretical analysis and experimental evaluation demonstrate that the framework can comprehensively improve the security of all kinds of user privacy, without compromising the availability of personalized information retrieval.