Mobile Networks and Management. 9th International Conference, MONAMI 2017, Melbourne, Australia, December 13-15, 2017, Proceedings

Research Article

A Personalized Multi-keyword Ranked Search Method Over Encrypted Cloud Data

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  • @INPROCEEDINGS{10.1007/978-3-319-90775-8_6,
        author={Xue Tian and Peisong Shen and Tengfei Yang and Chi Chen and Jiankun Hu},
        title={A Personalized Multi-keyword Ranked Search Method Over Encrypted Cloud Data},
        proceedings={Mobile Networks and Management. 9th International Conference, MONAMI 2017, Melbourne, Australia, December 13-15, 2017, Proceedings},
        proceedings_a={MONAMI},
        year={2018},
        month={5},
        keywords={Cloud computing Ciphertext search Multi-keyword search Relevance feedback},
        doi={10.1007/978-3-319-90775-8_6}
    }
    
  • Xue Tian
    Peisong Shen
    Tengfei Yang
    Chi Chen
    Jiankun Hu
    Year: 2018
    A Personalized Multi-keyword Ranked Search Method Over Encrypted Cloud Data
    MONAMI
    Springer
    DOI: 10.1007/978-3-319-90775-8_6
Xue Tian,*, Peisong Shen,*, Tengfei Yang,*, Chi Chen,*, Jiankun Hu1,*
  • 1: University of New South Wales at the Australian Defence Force Academy
*Contact email: tianxue@iie.ac.cn, shenpeisong@iie.ac.cn, yangtengfei@iie.ac.cn, chenchi@iie.ac.cn, J.Hu@adfa.edu.au

Abstract

Due to data privacy considerations, the data owners usually encrypt their documents before outsourcing to the cloud. The ability to search the encrypted documents is of great importance. Existing methods usually use the keywords to express users’ query intention, however it’s difficult for the users to construct a good query without the knowledge of document collection. This paper proposes a personalized ciphertext retrieval method based on relevance feedback, which utilizes user interaction to improve the correlation with the search results. The users only need to determine the relevance of the documents instead of constructing a good query, which can greatly improve the users query satisfaction. The selected IEEE published papers are taken as a sample of the experiment. The experimental results show that the proposed method is efficient and could raise the users’ satisfaction. Compared with MRSE-HCI method, our method could achieve higher precision rate and equally high efficiency performance.