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Privacy-Preserving Searchable Encryption Scheme Based on Deep Structured Semantic Model over Cloud Application

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Machine Learning for Cyber Security (ML4CS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13656))

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Abstract

With the continuous development of cloud computing and storage, searchable encryption as a key technology has attracted wide attention. However, existing statistical-based models face long document feature vectors and can not fully catch the document semantics information. This leads to inefficient and unsatisfactory retrieval results. In this paper, we propose a deep structured semantic model of ranked search over encrypted data (DSRSE) based on a convolutional neural network for the first time. In this scheme, document index vectors and query trapdoors are generated by the convolutional neural network model (CNNM) and encrypted by the secure kNN algorithm. In order to protect documents’ privacy security, we use private cloud servers to train the CNNM distributively. All parameters are updated constantly, and the CNNM is updated regularly. Furthermore, a clustering tree based index structure is proposed. Through inner product of document vectors, semantically similar files are clustered together for building a bottom-up index tree which enhances the retrieval efficiency. Analysis and experiments on real datasets illustrate that our schemes perform well in terms of privacy security, search efficiency and accuracy.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant Numbers: 62001055, 62102017, 61932014, 61972018 and the Fundamental Research Funds for the Central Universities (YWF-22-L-1273).

Author information

Authors and Affiliations

  1. School of Cyber Science and Technology, Beihang University, Beijing, 100191, China

    Na Wang, Shangcheng Zhang, Jianwei Liu, Kaifa Zheng & Jiawen Qiao

  2. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Jian Jiao

  3. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Junsong Fu

Authors
  1. Na Wang
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  3. Shangcheng Zhang
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Corresponding author

Correspondence to Junsong Fu .

Editor information

Editors and Affiliations

  1. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, IN, USA

    Yuan Xu

  2. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Huang Teng

  4. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  5. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Jin Li

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Wang, N. et al. (2023). Privacy-Preserving Searchable Encryption Scheme Based on Deep Structured Semantic Model over Cloud Application. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_49

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  • DOI: https://doi.org/10.1007/978-3-031-20099-1_49

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