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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. [ABSTRACT FROM AUTHOR]

Aiming at the privacy and security issues of sensitive speech data in cloud storage, and how to achieve efficient retrieval with privacy-preserving for encrypted speech data in cloud storage, a searchable encryption over encrypted speech retrieval scheme in cloud storage was proposed. Firstly, the data owner encrypts the original speech data adopting symmetric encryption algorithm Lorenz chaotic mapping. Secondly, the encrypted speech data is uploaded to the cloud server for storage, and meanwhile the Mel frequency cepstrum coefficient (MFCC) features of original speech are extracted as the input of the convolutional neural network (CNN) to execute the deep semantic feature extraction, which is taken as the speech keywords. Finally, the extracted speech keywords are encrypted and stored in the cloud. When the authorized user sends a retrieval request, the trained CNN is utilized to extract the keywords of the speech to be retrieved, and the search trapdoor is generated and sent to the cloud server. In the process of retrieval, the Euclidean distance is used to match the encrypted keywords with the search trapdoor. The theoretical analysis and experimental results show that the proposed scheme has higher security and retrieval accuracy, and can be suitable for the encrypted storage of speech data and efficient secure retrieval. [ABSTRACT FROM AUTHOR]