Abstract
Searchable Encryption is utilized to address the issue of searching for outsourced encrypted data on third-party untrusted cloud servers. Traditional approaches for ciphertext retrieval are limited to basic keyword-matching queries and fall short when it comes to handling complex semantic queries. Although several semantic retrieval schemes have been proposed in recent years, their performance is inadequate. This paper introduces a semantic retrieval scheme called CRE (Ciphertext Retrieval based on Encoder), which leverages the prompt-based RoBERTa pre-trained language model to generate precise embeddings for sentences in queries and documents. Moreover, to improve retrieval speed in the face of massive high-dimensional sentence embedding vectors, we introduce the HNSW algorithm. Through experimentation and theoretical analysis, this paper demonstrates that CRE outperforms \(SSSW_2\) and \(SSRB_2\) in terms of retrieval speed and accuracy.
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Zhang, K., Xu, S., Li, P., Zhang, D., Wang, W., Zou, B. (2024). CRE: An Efficient Ciphertext Retrieval Scheme Based on Encoder. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1966. Springer, Singapore. https://doi.org/10.1007/978-981-99-8148-9_10
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DOI: https://doi.org/10.1007/978-981-99-8148-9_10
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