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

Zongda Wu, Shigen Shen, Huxiong Li, Haiping Zhou, Chenglang Lu

Source Title: Journal of Organizational and End User Computing (JOEUC)33(6)

ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781799867494|DOI: 10.4018/JOEUC.292526

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MLA

Wu, Zongda, et al. "A Basic Framework for Privacy Protection in Personalized Information Retrieval: An Effective Framework for User Privacy Protection." JOEUC vol.33, no.6 2021: pp.1-26. http://doi.org/10.4018/JOEUC.292526

APA

Wu, Z., Shen, S., Li, H., Zhou, H., & Lu, C. (2021). A Basic Framework for Privacy Protection in Personalized Information Retrieval: An Effective Framework for User Privacy Protection. Journal of Organizational and End User Computing (JOEUC), 33(6), 1-26. http://doi.org/10.4018/JOEUC.292526

Chicago

Wu, Zongda, et al. "A Basic Framework for Privacy Protection in Personalized Information Retrieval: An Effective Framework for User Privacy Protection," Journal of Organizational and End User Computing (JOEUC) 33, no.6: 1-26. http://doi.org/10.4018/JOEUC.292526

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Abstract

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.