VMSE: Verifiable multi-keyword searchable encryption in multi-user setting supporting keywords updating

Liang, Yanrong; Li, Yanping; Zhang, Kai; Wu, Zhenqiang

doi:10.1016/j.jisa.2023.103518

Cited by 1 publication

(2 citation statements)

References 9 publications

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“…For different data, multiple legitimate users can access it together, but the retrieval results are not verified. Liang et al [12] proposed a multi-user and multi-keyword verifiable SE scheme, which uses hidden vector encryption technology to protect the privacy and verifies the results based on multi-user and multi-keyword, however, the fairness and efficiency of verification should be discussed.…”

Section: Related Workmentioning

confidence: 99%

“…While storing plaintext on cloud servers without being exposed, data users can achieve retrieval based on keywords, greatly improving the security of retrieval [7,8]. At the same time, the realization of multi-user and multi-keyword ciphertext retrieval technology is the current research hotspot [9][10][11][12]. However, some malicious cloud servers still access, leak, and maliciously tamper with without authorization, so it is necessary to verify the correctness of the returned results [13].…”

Section: Introductionmentioning

confidence: 99%

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Multi-User and multi-keyword verifiable encrypted speech retrieval method based on blockchain

Li,

Zhang,

Huang

et al. 2023

Preprint

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Aiming at the existing encrypted speech retrieval schemes that do not support multi-user multi-keyword retrieval, low retrieval efficiency and accuracy, lack of verifiability of encrypted speech retrieval results, and user fairness, a multi-user and multi-keyword verifiable encrypted speech retrieval method based on blockchain was proposed. Firstly, use the advanced encryption standard-128 (AES-128) to encrypt the speech and upload it to the cloud server. Secondly, the Mel-frequency cepstral coefficient (MFCC) and Filter bank (Fbank) speech features are extracted and fused into new MFCC-Fbank features, which are input into the designed CNN-BiGRU model for training. These features are used to extract more robust deep features to construct hash codes and are used as searchable encrypted keywords. Finally, use smart contracts to store encrypted speech hash codes and corresponding index values and manage multi-user keys through trusted institutions. When retrieval, smart contracts are used as trusted third parties for retrieval work, and the designed adaptive homomorphic Message Authentication Code (MAC) algorithm is used to verify the correctness of the retrieval results, ensuring fairness in verification and data privacy. The experimental results show that the proposed method effectively prevents user privacy leakage and improves encrypted speech retrieval accuracy and data security under multi-user and multi-keyword conditions.

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Section: Related Workmentioning

confidence: 99%