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.

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

How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective

With the boom in artificial intelligence (AI) and Internet-of-Things (IoT), thousands of smart devices are interconnected with each other and deeply applied into human society. This prosperity has significantly improved public service and management, which were traditionally based on manual work. As a notable scenario, librarianship has embraced an era of “Smart Libraries” enabled by AI and IoT. Unlike existing surveys, our work comprehensively overviews the AI- and IoT-based technologies in three fundamental aspects: smart service, smart sustainability, and smart security. We then further highlight the trend towards future smart libraries.

/pdf/a-survey-on-artificial-intelligence-aided-internet-of-things-20t68yb3.pdf

A Survey on Artificial Intelligence Aided Internet-of-Things Technologies in Emerging Smart Libraries

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, we 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. 

A Basic Framework for Privacy Protection in Personalized Information Retrieval

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.

How to protect reader lending privacy under a cloud environment: a technical method

The security of archival privacy data in the cloud has become the main obstacle to the application of cloud computing in archives management. To this end, aiming at XML archives, this paper aims to present a privacy protection approach that can ensure the security of privacy data in the untrusted cloud, without compromising the system availability.,The basic idea of the approach is as follows. First, the privacy data before being submitted to the cloud should be strictly encrypted on a trusted client to ensure the security. Then, to query the encrypted data efficiently, the approach constructs some key feature data for the encrypted data, so that each XML query defined on the privacy data can be executed correctly in the cloud.,Finally, both theoretical analysis and experimental evaluation demonstrate the overall performance of the approach in terms of security, efficiency and accuracy.,This paper presents a valuable study attempting to protect privacy for the management of XML archives in a cloud environment, so it has a positive significance to promote the application of cloud computing in a digital archive system.

A privacy protection approach for XML-based archives management in a cloud environment

Abstract In a digital library, an increasingly important problem is how to prevent the exposure of user privacy in an untrusted network. This study aims to design an effective approach for the protection of user privacy in a digital library, by consulting the basic ideas of encryption and anonymization. In our proposed approach, any privacy data, which can identify user’s real identity, should be encrypted first before being submitted to the library server, to achieve anonymization of user identity. Then, to solve the problem of querying encrypted privacy data, additional feature data are constructed for the encrypted data, such that much of the query processing can be completed at the server-side, without decrypting the data, thereby improving the efficiency of each kind of user query operation. Both theoretical analysis and experimental evaluation demonstrate the effectiveness of the approach, which can improve the security of users’ data privacy and behavior privacy on the untrusted server-side, without compromising the availability (i. e. accuracy, efficiency, and usability) of digital library services. This paper provides a valuable study attempt at the protection of digital library users’ privacy, which has a positive influence on the development of a privacy-preserving library in an untrusted network environment.

An Effective Approach for the Protection of User Privacy in a Digital Library

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A privacy protection approach for XML-based archives management in a cloud environment

An Effective Approach for the Protection of User Privacy in a Digital Library