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Semantic and secure search over encrypted outsourcing cloud based on BERT

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Abstract

Searchable encryption provides an effective way for data security and privacy in cloud storage. Users can retrieve encrypted data in the cloud under the premise of protecting their own data security and privacy. However, most of the current content-based retrieval schemes do not contain enough semantic information of the article and cannot fully reflect the semantic information of the text. In this paper, we propose two secure and semantic retrieval schemes based on BERT (bidirectional encoder representations from transformers) named SSRB-1, SSRB-2. By training the documents with BERT, the keyword vector is generated to contain more semantic information of the documents, which improves the accuracy of retrieval and makes the retrieval result more consistent with the user’s intention. Finally, through testing on real data sets, it is shown that both of our solutions are feasible and effective.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. U1836110 and U1836208); by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20200039.

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Authors and Affiliations

  1. Engineering Research Center of Digital Forensice, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Zhangjie Fu, Yan Wang & Xingming Sun

  2. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Zhangjie Fu, Yan Wang & Xingming Sun

  3. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Xiaosong Zhang

Authors
  1. Zhangjie Fu
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  2. Yan Wang
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  3. Xingming Sun
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  4. Xiaosong Zhang
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Corresponding author

Correspondence to Zhangjie Fu.

Additional information

Zhangjie Fu received his PhD in computer science from the College of Computer, Hunan University, China in 2012. He is currently a Professor at School of Computer and Software, Nanjing University of Information Science and Technology, China. He was a visiting scholar of Computer Science and Engineering at State University of New York at Buffalo from March, 2015 to March, 2016, USA. His research interests include Cloud and Outsourcing Security, Digital Forensics, Network and Information Security. His research has been supported by NSFC, PAPD, and GYHY. Zhangjie is a member of IEEE and a member of ACM.

Yan Wang received his BE in Internet of Things Engineering from Nanjing University of Information Science and Technology, China in 2018. He is currently pursuing his MS in computer science and technology at the Department of Computer and Software, Nanjing University of Information Science and Technology, China. His research interests include cloud security and information security.

Xingming Sun received the BS degree in mathematics from Hunan Normal University, China in 1984, the MS degree in computing science from the Dalian University of Science and Technology, China in 1988, and the PhD degree in computer science from Fudan University, China in 2001. He is currently a Professor with the School of Computer and Software, Nanjing University of Information Science and Technology, China. His research interests include network and information security, digital watermarking, cloud computing security, and wireless network security.

Xiaosong Zhang received the MS and PhD degrees in computer science from the University of Electronic Science and Technology of China (UESTC), China in 1999 and 2011, respectively. He is currently a Professor with the School of Computer Science and Engineering, University of Electronic Science and Technology of China, China. His research interests are blockchain, big data security, AI security, etc.

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Fu, Z., Wang, Y., Sun, X. et al. Semantic and secure search over encrypted outsourcing cloud based on BERT. Front. Comput. Sci. 16, 162802 (2022). https://doi.org/10.1007/s11704-021-0277-0

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  • DOI: https://doi.org/10.1007/s11704-021-0277-0

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