A retrieval algorithm of encrypted speech based on short-term cross-correlation and perceptual hashing

  • Qiu-yu ZhangEmail author
  • Liang Zhou
  • Tao Zhang
  • Deng-hai Zhang


In order to achieve extraction perceptual features from the encryption speech as a search digest for the content-based encryption speech retrieval, we present a retrieval algorithm of encrypted speech based on short-term cross-correlation and perceptual hashing in this paper. Firstly, the study encrypts the speech file and uploads the encrypted speech data to the encryption speech database in cloud server. Secondly, the sample speech clips are obtained by the cutting operation from the speech file for scrambling encryption. The perceptual hashing sequence of the encrypted speech is constructed by extracting the short-term cross-correlation of the encrypted speech signals as the search digest. These perceptual hashing sequences are uploaded into the hashing index table of cloud server. Finally, the Hamming distance algorithm is used for the matching retrieval operation during the search. The experimental results show that the proposed algorithm of encrypted speech perceptual hashing has a better discrimination, robustness and compactness, and the perceptual hashing sequences can be extracted directly from the encrypted sample speech. Meanwhile, the encryption speech signal has high recall and precision ratios after various content preserving operations. In the whole retrieval process, the downloading and decrypting operations of speech data are not necessary.


Encrypted speech retrieval Short-term cross-correlation coefficient Perceptual hashing Scrambling encryption Feature extraction of encrypted speech 



This work is supported by the National Natural Science Foundation of China (No. 61862041, No. 61363078), the Research Project in Universities of Education Department of Gansu Province (2017B-16, 2018A-187). The authors would like to thank the anonymous reviewers for their helpful comments and suggestions.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer and CommunicationLanzhou University of TechnologyLanzhouChina

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