In order to solve the problem of multiuser security sharing and privacy protection of the speech data in cloud storage and realize efficient encrypted speech retrieval, an encrypted speech retrieval scheme based on multiuser searchable encryption was proposed. Firstly, the ciphertext-policy attribute-based encryption (CP-ABE) and searchable encryption (SE) are combined to support a multiuser searchable speech encryption scheme, which achieves the encryption and fine-grained access control of the speech data. Secondly, the Mel frequency cepstral coefficient (MFCC) feature of the original speech that is used as the input of the long- and short-term memory network (LSTM) is extracted to perform the deep semantic feature extraction as the speech keywords. Finally, the speech keywords are encrypted to generate a secure index, bound together with the encrypted speech, and then stored in the cloud. During the user retrieving, the keywords of query speech are extracted by utilizing the trained LSTM to generate the search trapdoor of the user and then uploaded to the cloud server, and the Euclidean distance is used for matching the security index with the search trapdoor. In addition, a proxy server is introduced to execute the partial ciphertext decryption operations to reduce computation overhead and storage space. The theoretical analysis and experimental results show that the proposed scheme has higher security and retrieval accuracy and can realize the secure storage of the massive speech data and multiuser data sharing.

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In order to ensure the confidentiality and secure sharing of speech data, and to solve the problems of slow deployment of attribute encryption systems and fine-grained access control in cloud storage, a speech encryption scheme based on ciphertext policy hierarchical attributes was proposed. First, perform hierarchical processing of the attributes of the speech data to reflect the hierarchical structure and integrate the hierarchical access structure into a single-access structure. Second, use the attribute fast encryption framework to construct the attribute encryption scheme of the speech data, and use the integrated access to the speech data; thus, the structure is encrypted and uploaded to the cloud for storage and sharing. Finally, use the hardness of decisional bilinear Diffie–Hellman (DBDH) assumption to prove that the proposed scheme is secure in the random oracle model. The theoretical security analysis and experimental results show that the proposed scheme can achieve efficient and fine-grained access control and is secure and extensible.