Abstract
Encrypting data before outsourcing data has become a challenge in using traditional search algorithms. Many techniques have been proposed to cater the needs. However, as cloud service has a pay-as-you-go basis, these techniques are inefficiency. In this paper we attack the challenging problem by proposing an approximate multi keyword search with multi factor ranking over encrypted cloud data. Moreover, we establish strict privacy requirements and prove that the proposed scheme is secure in terms of privacy. To the best of our knowledge, we are the first who propose approximate matching technique on semantic search. Furthermore, to improve search efficiency, we consider multi-factor ranking technique to rank a query for documents. Through comprehensive experimental analysis combined with real world data, our proposed technique shows more efficiency and can retrieve more accurate results and meanwhile improve privacy by introducing randomness in query data.
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Acknowledgement
This work was partly supported by the Kennesaw State University College of Science and Mathematics the interdisciplinary Research Opportunities Program (IDROP), and the Office of the Vice President for Research (OVPR) Pilot/Seed Grant.
This was also partly supported by NSFC under No. 61772466, the Provincial Key Research and Development Program of Zhejiang, China under No. 2017C01055, the Fundamental Research Funds for the Central Universities, the Alibaba-Zhejiang University Joint Research Institute for Frontier Technologies (A.Z.F.T.) under Program No. XT622017000118, the CCF-Tencent Open Research Fund under No. AGR20160109, the National Key Research and Development Program of China (2016YFB0800201), and the Natural Science Fundation of Zhejiang Province (LY16F020016).
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He, J., Wu, Y., Xiang, G., Wu, Z., Ji, S. (2017). Efficient Privacy-Preservation Multi-factor Ranking with Approximate Search over Encrypted Big Cloud Data. In: Wen, S., Wu, W., Castiglione, A. (eds) Cyberspace Safety and Security. CSS 2017. Lecture Notes in Computer Science(), vol 10581. Springer, Cham. https://doi.org/10.1007/978-3-319-69471-9_33
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DOI: https://doi.org/10.1007/978-3-319-69471-9_33
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