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• W4315784913 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." @default.
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• W4315784913 date "2023-01-01" @default.
• W4315784913 modified "2023-09-24" @default.
• W4315784913 title "Privacy-Preserving Searchable Encryption Scheme Based on Deep Structured Semantic Model over Cloud Application" @default.
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• W4315784913 doi "https://doi.org/10.1007/978-3-031-20099-1_49" @default.
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