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In order to solve the problem of leakage in biometrics, to improve the performance of speech retrieval and the security of single biometric template, this paper proposes a verifiable speech retrieval algorithm based on diversity security template and BioHashing. Firstly, both of biometrics which are the cross-correlation cosine and improved sub-band energy entropy ratio are fused to construct the time-frequency biometric vectors, finally the vectors are classified by the K-medoids algorithm. Then, the dimension vectors of the orthogonal set matrix which is constructed by Schmidt orthogonalization of the QCNN(Quantum Cellular Neural Network) chaotic matrix are one-to-one corresponding to the biometric vectors of the corresponding class to form the diversity biosafety templates, and the templates are further quantified to generate BioHashing sequences. Finally, the address index of dimension vectors of QCNN chaotic matrix and BioHashing sequences of corresponding class are one to one correspondingly encrypted by QCNN chaotic mapping encryption to construct hash index, and then the hash index is stored in the system hash index table of the cloud server. At the same time, the encrypted speech is uploaded to the cloud sever after 2D-LICM chaotic scrambling-shift encryption which is convenient to match and retrieve data for the users. Experimental results show that the diversity template has better security and complexity by using K-medoids algorithm to classify the biometric. Hash index and encrypted speech can effectively prevent the leakage of privacy data. At the same time, the retrieval system has better discrimination and retrieval performance, and it performs verifiable retrieval of speech for content preservation operations. [ABSTRACT FROM AUTHOR]

Most of the existing Biohashing authentication algorithms extract perception feature from the original domain and store the generated hashing sequence in the cloud, which can easily cause the hashing sequence to be leaked. At the same time, the algorithm's robustness and matching accuracy under background noise are too low, leading to serious deviation in the authentication process. Therefore, this paper proposed an encrypted speech Biohashing authentication algorithm based on 4D hyperchaotic Bao system and feature fusion. Firstly, the algorithm encrypts the original speech by setting the key to the 4D hyperchaotic Bao system and transforms it into encrypted speech. Secondly, the preprocessed encrypted speech is processed by discrete wavelet transform (DWT) and spectral entropy to obtain the feature fusion matrix of the speech, and the pseudo-random matrix is generated by using two-dimensional Logistic ICMIC cascade mapping (2D-LICM). Then the feature fusion matrix and pseudo-random matrix are projected and mapped to construct the biometric template. Finally, the threshold is selected for binarization, and the Biohashing sequence is generated and uploaded to the cloud. In this way, a one-to-one correspondence is established between the encrypted speech and the hashing sequence. The experimental results show that the algorithm can directly extract the authentication digest from the encrypted speech. During the authentication process, the download and decryption of the speech data is not required, which improves the security of the hashing sequence storage. The encrypted speech can reduce the amplitude of signal change, has strong discrimination and robustness, and can resist complex background noise and has good real-time performance. [ABSTRACT FROM AUTHOR]

Purpose: To assess awareness and recognition of vestibular function tests in otorhinolaryngology medical staffs, especially the vestibular evoked myogenic potentials (VEMP) testing in patients with obstructive sleep apnea (OSA). Methods: A survey was delivered via either email or a social media app. The medical staffs of the Chinese Medical Association of Otolaryngology Head and Neck Surgery from various branches were enrolled. Study data were collected and managed with an online data collection tool. Results: A total of 1781 emails and 623 social media messages were sent to 2404 otorhinolaryngology medical staffs. One hundred and fifty-seven of them participated in the survey, including 24 via emails and 133 via the social media app. Regarding the knowledge of VEMP, only 59 (37.6%) of them agreed that OSA could be related to vertigo/dizziness/imbalance and 28 (17.8%) believed that OSA could result in VEMP abnormalities and would factor this in diagnosing the impairment of the vestibular function of OSA patients. A total of 7.6% of the respondents had never heard of the VEMP tests. Responses regarding the minimum age at which VEMP are possible ranged from younger than 6 months to greater than 18 years of age. Beliefs regarding the utility and reliability of VEMP varied, with 'unsure' being the most frequent response. In addition, only 17.8% of otolaryngologists indicated some access to the VEMP test. Conclusions: Knowledge and beliefs about the role of VEMP in diagnosing otolithic organ dysfunction caused by OSA in otorhinolaryngology vary widely. It is important for otorhinolaryngology medical staffs to learn the latest literatures and updated knowledge through continuing education. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile Internet, the dimension of speech data is too high and the space is complex. The existing speech retrieval algorithms can not meet the efficient retrieval efficiency and privacy security of speech data in massive applications. Aiming at the problems of retrieval efficiency and accuracy caused by high dimension and complex space of speech feature data, content verifiable retrieval after speech attack, and the security of speech storage and transmission process, a security framework based on KNN Secure Hash (KNNSH) is proposed for verifiable speech retrieval. In this algorithm, the spectral centroid of speech is used as the only input factor, and then KNN classification is used to train and learn the speech vector to obtain each speech centroid. Each speech centroid is assigned a specific hyperchaotic Lorenz compressed sensing encryption algorithm (HL-CS) key, and the security framework is constructed according to the revocable biometric template generated by the combination of classification and specific key. The binary hash vector is generated, and then the hash vector is encrypted by HL-CS. The same encryption algorithm is used to encrypt the original speech. Experimental results show that only one item needs to be matched in the intra class matching process after classification, which improves the retrieval efficiency and accuracy, and realizes the content verification of speech retrieval after content preservation operations. Speech encryption effectively prevents the disclosure of plaintext, ensures the security of speech storage and transmission process. It has a large key space, which is enough to resist exhaustive attacks. [ABSTRACT FROM AUTHOR]

The current speech content authentication process directly constructs the extracted biometrics into a binary hashing sequence and stores them in the cloud for hashing matching, so the authentication process is vulnerable to similarity attacks, which may lead to the disclosure of biometrics and hash sequences. In order to solve the above problem, this paper proposes an algorithm that is speech BioHashing security authentication algorithm based on CNN hyperchaotic map. Firstly, the user terminal conducts on linear prediction coefficients (LPC) analysis for the pre-processed speech signal, then extracts the improved Mel frequency cepstral coefficients (MFCC) features to obtains the new feature parameter, and calculates the Euclidean distance of the parameter as the biometric vector. Then, the biometrics and the orthogonal set matrix which is constructed by the improved random Fourier measurement matrix are inner product to form biosafety templates, and the templates are further quantified into BioHashing sequences. Finally, the BioHashing sequences are encrypted by CNN hyperchaotic map encryption algorithm and upload them to the cloud server. The experimental results show that the biosafety template not only provides the safe template for the biometric, but also the encryption algorithm of CNN hyperchaotic map guarantees the safety of the BioHashing sequence. At the same time, the algorithm has better robustness and discrimination, and tamper detection and location can better detect and locate mute attacks and substitution attacks. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of the stability analysis for fractional-order nonlinear systems. First, we investigate the asymptotic behavior of the system solution by discussing the positive limiting set for fractional-order systems. Then, sufficient conditions for the attraction of equilibrium point are obtained and these conditions lead to a corresponding asymptotic stability criterion. Moreover, by considering a constraint on Lyapunov functions, a criterion for globally asymptotic stability is derived as well. Finally, two illustrative examples are given to show the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

The existing speech authentication algorithms use plaintext speech for hashing structure, and save the generated hashing sequence in the cloud, which is easy to cause hashing sequence leakage. At the same time, the robustness and matching accuracy of speech signal under complex noise are low, which will cause serious deviations in authentication. In order to solve the above problems, this paper proposes an encrypted speech perceptual hashing authentication algorithm based on improved two-dimensional Henon chaotic map (2D-Henon) encryption and harmonic product spectrum (HPS). Firstly, the algorithm sets the key for the improved 2D-Henon and transforms the original speech to encrypted speech. And then, feature extraction is performed on the encrypted speech signal to obtain the HPS feature matrix, and perform mapping and dimensionality reduction on the HPS matrix. Finally, the threshold is selected for binarization, and the perceptual hashing sequence is constructed. Experimental results show that the algorithm adopts encrypted speech perceptual hashing sequence, which improves the security of hashing sequence storage. The encrypted speech signal can reduce the amplitude of signal change, which has strong discrimination and robustness, and also has better matching accuracy against complex noise. [ABSTRACT FROM AUTHOR]

This paper proposes a Encrypted speech retrieval based on long sequence Biohashing to solve the problem of plaintext data leakage in the existing speech retrieval system, and improve the efficiency and accuracy of speech retrieval, the diversity and revocability of biometric template. According to speech feature classification, a biometric template with a single mapping key is established, and then the feature vector is used to generate speech feature index, and the speech file is encrypted by the improved SHA256 algorithm, and finally, feature index and encrypted speech are sent to the cloud. For the input speech, the feature vector is generated at the mobile, and then the cloud will retrieve the feature vector table according to the feature vector to obtain the feature index of the speech, and finally, the feature index only matches the feature index related to the speech in the feature index table. Experimental results show that this algorithm can not only effectively prevent the leakage of plaintext, but also has good diversity and revocability of biometric template. At the same time, the algorithm not only has good efficiency and accuracy, but also solves the problem of speech retrieval after content preservation operation. [ABSTRACT FROM AUTHOR]

The existing speech authentication algorithms hash extracted speech features directly and saved them to the cloud, which is easy to cause speech feature leakage. In the process of constructing hashing, the utilization efficiency of speech feature is poor, and the short hashing sequence will lead to the lack of discrimination of hashing sequence and the deviation of authentication. In order to solve the above problems, a long sequence biometric hashing authentication algorithm based on two-dimensional Sine ICMI Cmodulation map (2D-SIMM) and constant Q cepstral coefficients (CQCC) cosine was proposed. First, this algorithm extracts the CQCC of the speech signal, then obtains the eigenvalue of the space cosine distance of the adjacent speech frame CQCC, and finally performs projection mapping between the eigenvalue and the pseudorandom matrix generated by 2D-SIMM to construct a biometric hashing sequence. This paper evaluates the proposed robust feature schemes of MFCC and CQCC space cosine distance through experiments. The experimental results show that CQCC spatial distance combined with 2D-SIMM biometrics characteristics can reach 10 - 21 . when the threshold is 0.35. The BER mean was only 0.0383 for maintaining the robustness of operation for different contents. When the SNR is -5 dB, the matching rate of different noises can reach 45%. At the same time, it also improves the security of the biological template, and the overall performance is greatly improved compared with the existing algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to solve the problems of poor security and small application scope of speech content authentication, and to improve the robustness, discrimination and real-time performance of speech authentication, a multi-format speech BioHashing algorithm based on energy to zero ratio and improved linear prediction minimum mean square error (LP-MMSE) parameter fusion is proposed. Firstly, the algorithm extracts the short-term logarithmic energy, zero-crossing rate and the covariance method's LP-MMSE of speech signal to be processed. Then, the time-frequency parameters are fused, and the fused feature vector and the orthogonal normalized random matrix of the key control are generated into BioHashing sequences through the inner product form. Finally, the BioHashing is encrypted by equal-length scrambling using henon chaotic map. The experimental results show that the proposed algorithm not only has the characteristics of good discrimination, strong robustness, good security, high real-time performance and wide application range, but also realizes the detection and localization of small-scale tampering of speech through minimum code distance (MCD) algorithm. At the same time, the algorithm also validates the unidirectionality of BioHashing with trapdoor by comparative difference method. [ABSTRACT FROM AUTHOR]

In this paper, an efficient retrieval approach for encrypted speech based on biological hashing and spectral subtraction is proposed. The proposed approach improves the impact of noise on the robustness and discrimination of speech hashing scheme, as well as improves the retrieval efficiency, accuracy, and security of search digests, and realizes the authentication of the query result. The speech owner firstly secure the original speech file by encrypting it with two-dimensional Arnold mapping and uploading it to the encrypted speech library on cloud. Then, the pre-processed speech signal is subjected to spectral subtraction and noise reduction, as well as the discrete wavelet transform (DWT) is performed to obtain the wavelet low-frequency coefficient and reconstruct the speech signal, calculating the normalized autocorrelation function to obtain the matrix feature vector, and using the Chebychew mapping algorithm to generate the pseudo-random matrix, and generate the pseudo-random Fourier matrix by fast Fourier transform (FFT). Finally, iterate the matrix feature vector and pseudo-random matrix. After the thresholding, the hash sequence is constructed and uploaded to the system hash index table on cloud. When speech's user retrieval, the Hamming distance algorithm is used for the matching retrieval operation during the search and integrity authentication of the query result. The experimental results show that the proposed approach effectively reduces the noise of speech, with strong robustness and discrimination, and the retrieval efficiency, accuracy and security have been significantly improved. [ABSTRACT FROM AUTHOR]

In order to solve the security problem of speech perception hash authentication, the application scope of speech authentication algorithm, and improve the robustness, discrimination and real-time authentication in the process of authentication, a multi-format speech BioHashing algorithm based on spectrogram is proposed. Firstly, the speech signal to be processed is converted into spectrogram and feature extraction is carried out by two-dimensional discrete cosine transform. Then, the dimensionality of the eigenvector is reduced by non-negative matrix factorization, and generation of BioHashing sequences by inner product of reduced dimension eigenvectors and orthogonal normalized random matrices. Finally, the BioHashing is encrypted by equal-length scrambling using Henon chaotic map. The algorithm also validates the unidirectionality of BioHashing with trapdoor by comparative difference method. The experimental results show that the proposed algorithm has the characteristics of good security, strong robustness, high real-time performance and wide application range. [ABSTRACT FROM AUTHOR]

In order to satisfy the requirements of retrieval time-efficiency and security for encrypted speech data retrieval in the cloud environment, and to improve the impact of noise on the robustness and discrimination for the speech perceptual hashing scheme, an encrypted speech retrieval algorithm based on Chirp-Z transform and perceptual hashing second feature extraction is proposed in this paper. The speech owner first processes the original speech file by pre-processing, framing, and adding window. The features of the original speech file is extracted by Chirp-Z transform combined with the sparse random matrix to construct a hash sequence. Then encrypt the original speech file based on the m sequence and upload it to the cloud to ensure the security of information. By processing the speech perceptual hashing feature, the speech features are re-extracted, and the speech is evenly classified by k-means clustering technique. The binary string of several hundred bits is converted into a decimal number. Finally, the second feature is stored in system hash index table of the cloud. When the user retrieves, the query speech is denoised and the hash sequence is extracted. Then the secondary features of the hash sequence are extracted and matched with the encrypted speech features in the cloud system hash index table to obtain the retrieval result. The experimental results show that the proposed algorithm greatly compresses the information capacity of speech features, significantly improves the retrieval time-efficiency, with strong robustness and discrimination, and has a good retrieval effect on noisy speech. [ABSTRACT FROM AUTHOR]

Aiming at the problems of existing speech authentication algorithms, such as poor robustness and discrimination, security vulnerability, low efficiency, poor ability of tamper detection and localization, a high-performance speech perceptual hashing authentication algorithm based on Discrete Wavelet Transform (DWT) and measurement matrix is proposed in this paper. Firstly, the speech signal is conducted with DWT by applying preprocessing, and the low-frequency wavelet coefficients are regarded as the perceptual feature value. Then the measurement matrix controlled by chaos map is applied to reduce the dimension of feature value. Finally, the feature value is used to generate the perceptual hash sequence by the process of hashing structure. The measurement matrix is designed as the secret key to enhance the security of the proposed algorithm. The experimental results demonstrates the proposed algorithm has high efficiency in perceptual robustness, discrimination, time consumption and security, as well as having a high accuracy of tampering detection and localization. [ABSTRACT FROM AUTHOR]

Combined with the linear prediction-minimum mean squared error (LP-MMSE), an efficient perceptual hashing algorithm based on improved spectral entropy for speech authentication was proposed in this paper. The linear prediction analysis is conducted on speech signal after preprocessing, framing and adding windows, and obtained the minimum mean squared error coefficient matrix. And then, the spectral entropy parameter matrix of each frame is calculated by using improved spectral entropy method. And the final binary perceptual hashing sequence is generated based on the above two matrices, and the speech authentication is completed. Comparing the experimental results of combining the Teager energy operator (TEO) with the linear predictive coefficients (LPC), LP-MMSE and line spectrum pair (LSP) coefficient respectively, it can be seen that the proposed algorithm had a good compromise between robustness, discrimination and authentication efficiency, and the proposed algorithm can meet the requirement of real-time speech authentication in speech communication. Experimental results show that the proposed algorithm was better than other existing methods in compactness. [ABSTRACT FROM AUTHOR]