A high security BioHashing encrypted speech retrieval algorithm based on feature fusion

摘要

In order to solve the problem of plaintext data leakage, and to improve the diversity and security of biometric template, this paper proposes a high security BioHashing encrypted speech retrieval algorithm based on feature fusion, and introduces K-means-KNN fusion algorithm to classify. Firstly, the features of speech are extracted through FFT and IFFT. Secondly, the fused features are classified and a single mapping secret key is assigned to each class. The improved Marotto chaos measurement matrix is generated by the secret key, and the BioHashing sequences are generated by iterating the measurement matrix with the feature data. Then, the speech clips are classified and a single mapping secret key is assigned to each class. The SPM(sine map and piece wise linear chaotic map) chaotic sequence is generated by the secret key and the speech clips are encrypted by the sequence. Finally, hash indexes and encrypted speech clips are uploaded to the cloud, the normalized Hamming distance algorithm is used for matching retrieval on the user terminal. Experimental results show that the algorithm not only effectively prevents plaintext data leakage, but also achieves 100% retrieval accuracy for the original speech clips. Moreover, there are 18 classes of biometric templates, which have good security and key revocability.