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This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for user preference privacy protection in terms of security, usability, efficiency, and accuracy. Then, we comprehensively review the technical features for all kinds of popular methods for user privacy protection, and analyze their application limitations in personalized information retrieval, according to the constraints of preference privacy protection. The results show that personalized information retrieval has higher requirements for users’ privacy protection, i.e., it is required to comprehensively improve the security of users’ preference privacy on the untrusted server-side, under the precondition of not changing the platform, algorithm, efficiency, and accuracy of personalized information retrieval. However, all kinds of existing privacy methods still cannot meet the above requirements. This paper is an important study attempt to the problem of user preference privacy protection of personalized information retrieval, which can provide a basic reference and direction for the further study of the problem.

PurposeIn this paper, the authors propose an effective mechanism for the protection of digital library readers' lending privacy under a cloud environment.Design/methodology/approachThe basic idea of the method is that for each literature circulation record, before being submitted to the untrusted cloud database of a digital library for storage, its reader number has to be encrypted strictly at a client, so as to make it unable for an attacker at the cloud to know the specific reader associated with each circulation record and thus protect readers' lending privacy. Moreover, the authors design an effective method for querying the encrypted literature circulation records, so as to ensure the accuracy and efficiency of each kind of database queries related to the encrypted reader number field of literature circulation records.FindingsFinally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed methods.Originality/valueThis paper presents the first study attempt to the privacy protection of readers' literature circulations, which can improve the security of readers' lending privacy in the untrusted cloud, without compromising the accuracy and efficiency of each kind of database queries in the digital library. It is of positive significance to construct a privacy-preserving digital library platform.

Along with the rapid development of network technologies, the server-side of an e-commerce website is becoming more and more untrustworthy. Thus, how to prevent the disclosure of users’ behavior privacy in online business activities has attracted people’s wide attention. Aiming at the protection of users’ commodity viewing privacy in a commercial website, this paper proposes to construct a group of dummy requests on a trusted client, then, which are submitted together with a user commodity viewing request to the untrusted server-side, so as to confuse and cover up the user preferences. First, we define a privacy model for a user commodity viewing service, in which we introduce a concept called entropy for commodity viewing probability to measure the confusion effect of dummy requests on user requests, and we introduce a concept called regional distance among commodity categories to measure the cover-up effect of dummy requests on users’ commodity viewing preferences. Second, we design an implementation algorithm to generate a group of ideal dummy requests that can meet the constraints formulated in the privacy model. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed approach, i.e., which can improve the security of users’ commodity viewing privacy on the untrusted server-side, without compromising the availability of an e-commerce website. In this paper, we present a valuable research attempt to the protection of users’ behavior privacy in a commercial website, which is of positive significance for building a privacy-preserving e-commerce platform.