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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.

Due to the advantages of pay-on-demand, expand-on-demand and high availability, cloud databases (CloudDB) have been widely used in information systems. However, since a CloudDB is distributed on an untrusted cloud side, it is an important problem how to effectively protect massive private information in the CloudDB. Although traditional security strategies (such as identity authentication and access control) can prevent illegal users from accessing unauthorized data, they cannot prevent internal users at the cloud side from accessing and exposing personal privacy information. In this paper, we propose a client-based approach to protect personal privacy in a CloudDB. In the approach, privacy data before being stored into the cloud side, would be encrypted using a traditional encryption algorithm, so as to ensure the security of privacy data. To execute various kinds of query operations over the encrypted data efficiently, the encrypted data would be also augmented with additional feature index, so that as much of each query operation as possible can be processed on the cloud side without the need to decrypt the data. To this end, we explore how the feature index of privacy data is constructed, and how a query operation over privacy data is transformed into a new query operation over the index data so that it can be executed on the cloud side correctly. The effectiveness of the approach is demonstrated by theoretical analysis and experimental evaluation. The results show that the approach has good performance in terms of security, usability and efficiency, thus effective to protect personal privacy in the CloudDB.

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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.