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Most encrypted speech retrieval algorithms are over-optimized for discriminability and robustness, which leads to poor security and efficiency. And it's inefficient to compute large amounts of data in a single machine. Therefore, in this paper, based on the traditional model of ciphertext speech retrieval system, an efficient encrypted speech retrieval based on Hadoop cluster under SW CPU is proposed. The study uses the SW CPU as a cloud and introduces the Hadoop cluster technology. In the proposed algorithm, firstly, the peak frequency and spectral crest factor of the speech are extracted and fused. Secondly, the hyper chaotic measurement matrix is generated by the key so that it is iterated with the feature vectors and further binarized to generate the BioHashing sequence. A pseudo-random sequence is generated by the key, mapping encryption is performed on the speech segments to generate the encrypted speech, and linear-feedback shift register (LSFR) encryption is performed on the BioHashing sequence to generate the hash index. Finally, the hash index and encrypted speech are uploaded to the cloud via WinSCP. In the SW CPU, multi-processor simultaneous operation can speed up the processing of large amounts of data. The experimental results show that the proposed BioHashing algorithm has a good compromise relationship and the proposed system model has a good security. Moreover, the Hadoop cluster technology effectively improves the retrieval performance. [ABSTRACT FROM AUTHOR]

Existing biohashing authentication schemes usually store bioinformation in the cloud, whose storage method can face the problems of bioinformation leakage and tampering, and single point of failure. To address the above problems, a ciphertext speech biohashing authentication scheme based on chaotic system and improved public chain is proposed. First, the fusion features are extracted and generated with the inner product of 3D chaotic system to generate the biohashing sequence. Second, the original speech is encrypted and uploaded to the InterPlanetary file system and then, uploaded to the blockchain after the data are encrypted. Finally, the server matches the hash sequence queried on the chain with the to-be-authenticated hash at the user's end using the Hamming distance. The experimental results show that the biohashing algorithm in this paper has better distinguishability, robustness, security and time efficiency. The key space of the speech encryption algorithm is 2 256 , which can well resist the exhaustive attack and ensure the security during speech authentication. In this paper, the combination of on-chain and off-chain storage of speech information can prevent the problem of bioinformation leakage and tampering, as well as avoiding the problem of single point of failure. [ABSTRACT FROM AUTHOR]

Telecare Medical Information System (TMIS) is gaining importance in the present COVID-19 crisis. TMIS as a technology, offers patients a range of remote medical services, incorporated into Wireless Body Area Network (WBAN). The patient's medical report is confidentially transmitted over an open channel in TMIS environments. An attacker may attempt to compromise the security, such as forgery, replay, and impersonation attacks. To ensure secure communication, various authentication solutions have been introduced for TMIS. Biometrics and Elliptic Curve Cryptography-based mutual authentication protocol was recommended by Sahoo et al. (2020) and is proved to have some loopholes in the protocol. We discovered, however, Sahoo et al. method is unable to prevent privileged insider attacks and insider attacks along with patient anonymity. Jongseok Ryu et al. recommended a ECC based three-factor mutual authentication protocol and ensures patient's confidentiality for TMIS with proof of informal analysis. They have also performed formal security studies utilizing the Automated Validation of Internet Security Protocols and Applications (AVISPA), the Burrows-Abadi-Needham (BAN) logic and Real-Or-Random (ROR) model. However, we have reviewed the Jongseok Ryu et al.'s proposal. Based on his attacker model, we have examined that this scheme is unsafe against Message Substitution Attacks, Man-in-the-Middle attacks, Session Key Disclosure attacks, Privileged Insider attacks, and Stolen verifier attacks. we suggest a technique to be safe from the above security threats. [ABSTRACT FROM AUTHOR]

Telecare Medical Information System (TMIS) is gaining importance in the present COVID-19 crisis. TMIS as a technology, offers patients a range of remote medical services, incorporated into Wireless Body Area Network (WBAN). The patient’s medical report is confidentially transmitted over an open channel in TMIS environments. An attacker may attempt to compromise the security, such as forgery, replay, and impersonation attacks. To ensure secure communication, various authentication solutions have been introduced for TMIS. Biometrics and Elliptic Curve Cryptography-based mutual authentication protocol was recommended by Sahoo et al. (2020) and is proved to have some loopholes in the protocol. We discovered, however, Sahoo et al. method is unable to prevent privileged insider attacks and insider attacks along with patient anonymity. Jongseok Ryu et al. recommended a ECC based three-factor mutual authentication protocol and ensures patient’s confidentiality for TMIS with proof of informal analysis. They have also performed formal security studies utilizing the Automated Validation of Internet Security Protocols and Applications (AVISPA), the Burrows–Abadi–Needham (BAN) logic and Real-Or-Random (ROR) model. However, we have reviewed the Jongseok Ryu et al.’s proposal. Based on his attacker model, we have examined that this scheme is unsafe against Message Substitution Attacks, Man-in-the-Middle attacks, Session Key Disclosure attacks, Privileged Insider attacks, and Stolen verifier attacks. we suggest a technique to be safe from the above security threats.

Early bird Template protection using cancelable biometrics prevents data loss and hacking stored templates, by providing considerable privacy and security. Hashing and salting techniques are used to build resilient systems. Salted password method is employed to protect passwords against different types of attacks namely brute-force attack, dictionary attack, rainbow table attacks. Salting claims that random data can be added to input of hash function to ensure unique output. Hashing are speed bumps in an attacker's road to breach user's data. Research proposes a contemporary two factor authenticator called Biohashing. Biohashing procedure is implemented by recapitualted inner product over a pseudo random number generator key, as well as fingerprint features that are a network of minutiae. Cancelable template authentication used in fingerprint-based sales counter accelerates payment process. Fingerhash is code produced after applying biohashing on fingerprint. Fingerhash is a binary string procured by choosing individual bit of sign depending on a preset threshold. Experiment is carried using benchmark FVC 2002 DB1 dataset. Authentication accuracy is found to be nearly 97%. Results compared with state-of-art approaches finds promising. [ABSTRACT FROM AUTHOR]

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]

In the recent COVID-19 situation, Telecare Medical Information System (TMIS) is attracting attention. TMIS is one of the technologies used in Wireless Body Area Network (WBAN) and can provide patients with a variety of remote healthcare services. In TMIS environments, sensitive data of patients are communicated via an open channel. An adversary may attempt various security attacks including impersonation, replay, and forgery attacks. Therefore, numberous authentication schemes have been suggested to provide secure communication for TMIS. Sahoo et al. proposed a mutual authentication scheme based on biometrics and Elliptic Curve Cryptography (ECC) in 2020. However, we find out that Sahoo et al.’s scheme cannot resist insider and privileged insider attacks and cannot guarantee patient anonymity. In this paper, we propose a secure ECC-based three-factor mutual authentication protocol that guarantees the privacy of patients for TMIS. We conduct informal security analysis to prove that our protocol is secure from various security attacks. In addition, we perform formal security analyses using the Automated Validation of Internet Security Protocols and Applications (AVISPA), Burrows-Abadi-Needham (BAN) logic, and the Real-Or-Random (ROR) model. Furthermore, we assess our protocol’s performance and compare it to other protocols. As a result, our protocol has lower communication costs, and better security features compared to related existing protocols. Therefore, our protocol is more appropriate for TMIS environments than other related protocols.

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 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]

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]

The COVID-19 outbreak and its medical distancing phenomenon have effectively turned the global healthcare challenge into an opportunity for Telecare Medical Information Systems. Such systems employ the latest mobile and digital technologies and provide several advantages like minimal physical contact between patient and healthcare provider, easy mobility, easy access, consistent patient engagement, and cost-effectiveness. Any leakage or unauthorized access to users' medical data can have serious consequences for any medical information system. The majority of such systems thus rely on biometrics for authenticated access but biometric systems are also prone to a variety of attacks like spoofing, replay, Masquerade, and stealing of stored templates. In this article, we propose a new cancelable biometric approach which has tentatively been named as "Expression Hash" for Telecare Medical Information Systems. The idea is to hash the expression templates with a set of pseudo-random keys which would provide a unique code (expression hash). This code can then be serving as a template for verification. Different expressionswould result in different sets of expression hash codes, which could be used in different applications and for different roles of each individual. The templates are stored on the server-side and the processing is also performed on the server-side. The proposed technique is a multi-factor authentication system and provides advantages like enhanced privacy and security without the need formultiple biometric devices. In the case of compromise, the existing code can be revoked and can be directly replaced by a new set of expression hash code. The well-known JAFFE (The Japanese Female Facial Expression) dataset has been for empirical testing and the results advocate for the efficacy of the proposed approach. [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 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]

As one of the research hotspots in the field of speech recognition, content-based speech retrieval algorithms can detect speech information with the same content features, which improves computer intelligence while reducing labor costs, and thus have been widely used. Although most of the current speech content retrieval algorithms can guarantee excellent retrieval performance for small-scale speech retrieval work, the performance of the above algorithms is greatly reduced under the constraints of large speech data storage space and high content redundancy. In order to solve the above problems, a high-performance speech BioHashing retrieval algorithm based on audio segmentation is proposed in this paper. The algorithm is divided into an offline pre-processing phase and an online retrieval phase, The offline pre-processing stage converts the speech data into BioHashing sequences with speech content characteristics. In this process, first of all, the Power-Normalized Cepstral Coefficients (PNCC) features of the speech data are extracted and biometric templates with single mapping keys are constructed according to the PNCC features, obtaining BioHashing sequences. Then, slice the original speeches into short-time audio segments according to the proposed audio segmentation algorithm, and the hash reconstruction operation is performed on the BioHashing sequences to obtain the reconstructed Hashing sequences for online retrieval. The online search phase responds to the users' query requests, just find the hash index that matches the query hash sequence from the BioHashing index table, and will the standardized editing distance (SED) to the closest 1 value corresponding to the hash index as the retrieval result back to the user. The experimental results show that the reconstructed hash sequences obtained after removing the silent redundant segments have better robustness and discrimination. Moreover, the algorithm achieves 100% retrieval accuracy for the original speech clips, and the average retrieval time is only 0.0157 s, which shows that the algorithm has good retrieval performance and can meet the needs of speech retrieval in various environments. • The audio segmentation algorithm is applied to speech retrieval. • Using normalized editing distances to match hash sequences of different lengths. • The machine learning classification algorithm is applied to the speech feature data. • Constructing revocable biometric templates, implemented secondary distribution retrieval. [ABSTRACT FROM AUTHOR]

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Online access has been widely adopted to distribute diversified services to customers. In this architecture, public channels are utilized to exchange information between end users and remote servers at anytime and anywhere. To achieve confidentiality and integrity for transferred data, the related parties have to authenticate each other and negotiate a secret session key to encrypt and decrypt exchanged messages. Since the Lamport's pioneering authentication work in 1981, numerous mechanisms have been proposed to enhance security as well as reduce computation and payload data. Recently, Chuang and Chen proposed a multi-server authenticated agreement protocol employing a smart card and biometric data to eliminate the weaknesses caused by parameters related to low-entropy human-memorable passwords that are stored in a physical location. However, Mishra et al. showed that Chuang and Chen's protocol is not only vulnerable to multiple attacks but also suffers from the drawback of variation of biometric data. To overcome these weaknesses, they proposed an enhanced three-factor authenticated key agreement protocol using the low-error rate Biohashing technique. Unfortunately, we found that Mishra et al.'s scheme is also vulnerable to the denial-of-service attack, the traceable user attack, the impersonation attack, and the pre-shared key attack. Furthermore, the protocol does not provide any user revocation mechanism to control user accesses. In this novel untraceable authenticated key agreement scheme, we adopt the Hamming distance to verify encrypted Biohash codes and a public-key technique to construct the revocation mechanism. Our scheme achieves not only zero errors of biometric verification but also secure against all known attacks. [ABSTRACT FROM AUTHOR]

As for the main drawback of the original form of BioHashing is low performance when an imposter obtains a legitimate token and uses it to claim as a genuine. This paper, is circumvented the problem by an improved BioHashing for protection of fingerprint template, which could generate fixed long binary sequences nonlinearly in the process of quantitative. It improved the security of the algorithm by comparing the nonlinear process. The results of theory analyzing and simulation show that the proposed schemes can effectively protect the security of the fingerprint template of sensitive data and increase the recognition performance of the system. [ABSTRACT FROM AUTHOR]

Telecare medicine information system (TMIS) offers the patients convenient and expedite healthcare services remotely anywhere. Patient security and privacy has emerged as key issues during remote access because of underlying open architecture. An authentication scheme can verify patient's as well as TMIS server's legitimacy during remote healthcare services. To achieve security and privacy a number of authentication schemes have been proposed. Very recently Lu et al. ( J. Med. Syst. 39(3):1-8, ) proposed a biometric based three factor authentication scheme for TMIS to confiscate the vulnerabilities of Arshad et al.'s ( J. Med. Syst. 38(12):136, ) scheme. Further, they emphasized the robustness of their scheme against several attacks. However, in this paper we establish that Lu et al.'s scheme is vulnerable to numerous attacks including (1) Patient anonymity violation attack, (2) Patient impersonation attack, and (3) TMIS server impersonation attack. Furthermore, their scheme does not provide patient untraceability. We then, propose an improvement of Lu et al.'s scheme. We have analyzed the security of improved scheme using popular automated tool ProVerif. The proposed scheme while retaining the plusses of Lu et al.'s scheme is also robust against known attacks. [ABSTRACT FROM AUTHOR]

Wide spread use of biometric based authentication implies the need to secure biometric reference data. Various template protection schemes have been introduced to prevent biometric forgery and identity thefts. Cancelable biometrics is a recent approach introduced to address the concerns regarding privacy of biometric data, public confidence, and acceptance of biometric systems. It allows biometric templates to be cancelled and revoked like passwords innumerable times. The work proposes a novel cancelable biometric template generation algorithm using Gaussian random vectors and one way modulus hashing. Instead of using the original templates, the proposed system uses its transformed versions for storing and matching. The approach is tested on face and palmprint biometric modalities. A thorough analysis is performed to analyze the performance, non-invertibility, and distinctiveness of the proposed approach which reveals that the generated templates are non-invertible, easy to revoke, and also deliver good performance. [ABSTRACT FROM AUTHOR]

The healthcare sector has witnessed robust growth in the realm of medical information and its application during the last two decades. Consequently, the universal availability of patient records using web-based technology has acquired great significance in providing quality healthcare services on a global scale. With paramount aspects such as medical records confidentiality, it is an important consideration of modern times to secure medical information like CT-scan, X-Ray, and MRI when transmitted across public networks. Combining watermarking and BioHashing in medical image protection provides the means to safely transmit data over the internet and enhance authentication. In this context, we present a BioHashing and watermarking-based technique capable of providing integrity, authenticity, and confidentiality to different medical images. At the sender side, first, the BioHashing method is used to generate the watermark which is then embedded in weber local descriptor excitation component images using discrete cosine transform. The proposed technique meets all the requirements of robustness and imperceptibility. The experimental and comparison results demonstrate that the proposed method is superior to existing methods. • In this paper, a novel robust medical images information protection method has been introduced. • Medical images are secured using the proposed method even if it is attacked during the transmission process. • The proposed method combine Biohashing with watermarking to make the medical image more secure. • The experimental results suggested the proposed method meets all the requirements of robustness and imperceptibility. [ABSTRACT FROM AUTHOR]

In the light of recent security incidents, leading to compromise of services using single factor authentication mechanisms, industry and academia researchers are actively investigating novel multi-factor authentication schemes. Moreover, exposure of unprotected authentication data is a high risk threat for organizations with online presence. The challenge is how to ensure security of multi-factor authentication data without deteriorating the performance of an identity verification system? To solve this problem, we present a novel framework that applies random projections to biometric data (inherence factor), using secure keys derived from passwords (knowledge factor), to generate inherently secure, efficient and revocable/renewable biometric templates for users׳ verification. We evaluate the security strength of the framework against possible attacks by adversaries. We also undertake a case study of deploying the proposed framework in a two-factor authentication setup that uses users׳ passwords and dynamic handwritten signatures. Our system preserves the important biometric information even when the user specific password is compromised – a highly desirable feature but not existent in the state-of-the-art transformation techniques. We have evaluated the performance of the framework on three publicly available signature datasets. The results prove that the proposed framework does not undermine the discriminating features of genuine and forged signatures and the verification performance is comparable to that of the state-of-the-art benchmark results. [ABSTRACT FROM AUTHOR]

The article analyzes the security weaknesses of the biometric remote user authentication scheme proposed by Awasthi and Srivastava for the telecare medicine information system (TMIS). It proposes a more secure modified scheme with a simulation for formal security verification using Automated Validation of Internet Security Protocols and Applications (AVISPA). It cites that the enhanced scheme uses a secure one-way chaotic hash function and a secure biohashing function.

For the difficulty of key management in the traditional encryption system, this paper put forward a novel key binding scheme using the fingerprint to manage the key. It firstly converted the fingerprint feature into a string of binary sequence by the BioHashing method, and then performed an improved shuffling algorithm to scramble the binary sequence, after that it binded the key to the scrambled sequence by a Fuzzy Vault scheme. The simulation result shows that by the error tolerance ability of the Fuzzy Vault along with the error correction ability of the BCH error-correcting code, the scheme can restore the key correctly under the condition that fingerprints are safe. Meanwhile, the improved shuffling algorithm implements the irreversibility and the cancellability of the fingerprint template. [ABSTRACT FROM AUTHOR]

Biometrics-based authentication is an effective approach which is used for automatically recognizing a person's identity. Recently, it has been found that the finger-knuckle-print (FKP), which refers to the texture pattern produced by the finger knuckle bending, is highly unique and can be used as a biometric identifier. In this paper, we present an effective FKP recognition scheme for personal identification and identity verification. This method is a new encoding scheme based on local binary pattern (LBP). Each image first is decomposed in several blocks, each block is convolved with a bank of Gabor filters and then, the LBPs histograms are extracted from the convolved images. Finally, a BioHashing approach is applied on the obtained fixed-length feature vectors. Extensive experiments conducted over the Poly-U FKP database demonstrated the efficiency and effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Abstract: In this work an on-line signature authentication system based on an ensemble of local, regional, and global matchers is presented. Specifically, the following matching approaches are taken into account: the fusion of two local methods employing Dynamic Time Warping, a Hidden Markov Model based approach where each signature is described by means of its regional properties, and a Linear Programming Descriptor classifier trained by global features. Moreover, a template protection scheme employing the BioHashing and the BioConvolving approaches, two well known template protection techniques for biometric recognition, is discussed. The reported experimental results, evaluated on the public MCYT signature database, show that our best ensemble obtains an impressive Equal Error Rate of 3%, when only five genuine signatures are acquired for each user during enrollment. Moreover, when the proposed protected system is taken into account, the Equal Error Rate achieved in the worst case scenario, that is,when an “impostor” is able to steal the hash keys, is equal to 4.51%, whereas an Equal Error Rate ∼0 can be obtained when nobody steals the hash keys. [Copyright &y& Elsevier]

Abstract: Most authentication process uses password and personal identification numbers (PIN) for security purposes. In order to remove the problem of hacking or stealing of the password and PIN numbers, there has been an increased interest in the utilization of specific biometric feature of the user. Recently, biohashing systems have been introduced for automatic biometric recognition. In a biohashing system, biohash codes are generated using the feature of the biometric. A basic biohashing system involves two steps. First is the extraction of the feature from the input biometric image and second is the discretisation of the obtained feature vector by using ortho-normalized random numbers. In this paper, a new biohashing system has been proposed in which joint transform correlator (JTC) has been used for extraction of the specific feature of the biometric. In the enrolment process, a biohash code has been generated by using a single face image and then stored. In the verification process, this biohash code is matched with the verification codes for recognition purpose. The main advantage of the proposed biohashing method is the possibility of the optical implementation of the feature extraction of the face image. Experimental as well as simulation results have been given to validate the proposed technique. Normalized Hamming distance has been calculated to discriminate the genuine and impostor face images. By varying the dimension of the feature matrix, the study of the variation of the normalized Hamming distance with the density of the population has been undertaken. For the performance evaluation of the proposed technique the false rejection ratio (FRR) and false acceptance ratio (FAR) have also been calculated. [Copyright &y& Elsevier]

Abstract: In this work, we present a novel hybrid fingerprint matcher system based on local binary patterns. The two fingerprints to be matched are first aligned using their minutiae, then the images are decomposed in several overlapping sub-windows, each sub-window is convolved with a bank of Gabor filters and, finally, the invariant local binary patterns histograms are extracted from the convolved images. Extensive experiments conducted over the four FVC2002 fingerprint databases show the effectiveness of the proposed hybrid approach with respect to the well-known Tico''s minutiae matcher and other image-based approaches. Moreover, a BioHashing approach have been designed using the proposed fixed-length feature vector and very interesting performance has been obtained by combining it with the Tico''s minutiae matcher. [Copyright &y& Elsevier]

Abstract: In this work, we propose a multi-modal method that combines the scores of selected fingerprint matchers with the score obtained by a palm authenticator where the palm features are combined with pseudo-random numbers. We use a random subspace of AdaBoost.M1 to combine the scores of the best fingerprint matchers automatically selected among the competitors of FVC2004, and we study the performance when a different number of competitors are involved in the fusion. Moreover a deep study is carried out to design a hybrid system based on the combination of palm image features and a personal key. In conclusion, the aim of this work is to design a multi-modal biometric system and to analyze the benefits and the limits of fusion approaches in order to boost the performance of hybrid system in the worst testing hypothesis when an “impostor” steal the personal key of the user A before he tries to authenticate as A. The experimental results reported in this paper confirm that using a multi-modal ensemble of matchers it is possible to overcome some of the limitations of each single matcher leading to a considerable performance improvement. [Copyright &y& Elsevier]

Abstract: Verification based on tokenised pseudo-random numbers and user specific biometric feature has received much attention. In this paper, we propose a BioHashing system for automatic face recognition based on Fisher-based Feature Transform, a supervised transform for dimensionality reduction that has been proved to be very effective for the face recognition task. Since the dimension of the Fisher-based transformed space is bounded by the number of classes – 1, we use random subspace to create K feature spaces to be concatenated in a new higher dimensional space, in order to obtain a big and reliable “BioHash code”. [Copyright &y& Elsevier]

Abstract: Given the recent explosion of interest in human authentication, verification based on tokenized pseudo-random numbers and the user specific biometric feature (BioHashing) has received much attention. These methods have significant functional advantages over sole biometrics i.e. zero equal error rate. The main drawback of the base BioHashing method proposed in the literature relies in exhibiting low performance when an “impostor”B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we introduce some ideas to improve the base BioHashing approach in order to maintain a very low equal error rate when nobody steals the Hash key, and to reach good performance also when an “impostor” steals the Hash key. [Copyright &y& Elsevier]

Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the integration of external (password or token-derived) randomness with user-specific biometrics, resulting in bitstring outputs with security characteristics (i.e., noninvertibility) comparable to cryptographic ciphers or hashes. The resultant BioHashes are hence cancellable, i.e., straightforwardly revoked and reissued (via refreshed password or reissued token) if compromised. BioHashing furthermore enhances recognition effectiveness, which is explained in this paper as arising from the Random Multispace Quantization (RMQ) of biometric and external random inputs. [ABSTRACT FROM AUTHOR]

Abstract: As a result of the growing demand for accurate and reliable personal authentication, biometric recognition, a substitute for or complement to existing authentication technologies, has attracted considerable attention. It has recently been reported that, along with its variants, BioHashing, a new technique that combines biometric features and a tokenized (pseudo-) random number (TRN), has achieved perfect accuracy, having zero equal error rates (EER) for faces, fingerprints and palmprints. There are, however, anomalies in this approach. These are identified in this paper, in which we systematically analyze the details of the approach and conclude that the claim of having achieved a zero EER is based upon an impractical hidden assumption. We simulate the claimants’ experiments and find that it is not possible to achieve their reported performance without the hidden assumption and that, indeed, the results are worse than when using the biometric alone. [Copyright &y& Elsevier]

Human authentication is the security task whose job is to limit access to physical locations or computer network only to those with authorisation. This is done by equipped authorised users with passwords, tokens or using their biometrics. Unfortunately, the first two suffer a lack of security as they are easy being forgotten and stolen; even biometrics also suffers from some inherent limitation and specific security threats. A more practical approach is to combine two or more factor authenticator to reap benefits in security or convenient or both. This paper proposed a novel two factor authenticator based on iterated inner products between tokenised pseudo-random number and the user specific fingerprint feature, which generated from the integrated wavelet and Fourier–Mellin transform, and hence produce a set of user specific compact code that coined as BioHashing. BioHashing highly tolerant of data capture offsets, with same user fingerprint data resulting in highly correlated bitstrings. Moreover, there is no deterministic way to get the user specific code without having both token with random data and user fingerprint feature. This would protect us for instance against biometric fabrication by changing the user specific credential, is as simple as changing the token containing the random data. The BioHashing has significant functional advantages over solely biometrics i.e. zero equal error rate point and clean separation of the genuine and imposter populations, thereby allowing elimination of false accept rates without suffering from increased occurrence of false reject rates. [Copyright &y& Elsevier]

Nowadays, biometric-based authentication is gaining immense popularity due to the widespread usage of digital activities. Among various biometric traits, the iris is one of the most discriminative, accurate, and popularly used biometrics. However, due to its immutable nature, it is highly vulnerable to adversarial attacks if stolen and thus poses a severe security threat. Here, in this work, we propose a cancelable iris biometric authentication system that stores a transformed version of the original iris template and thus enables cancelation and re-enrolment in case if the original template is stolen. Firstly, for extracting discriminative iris features, we have proposed a novel deep architecture based on aggregation learning. This deep architecture makes use of qualitative measure (ordinal measure), unlike popularly used quantitative measures. The usage of ordinal measures in this work enables to encode distinctive iris features quite well. Later generated iris features are protected using state-of-the-art two representative cancelable biometric techniques, namely BioHashing and 2 N discretized BioPhasor. Finally, in order to justify the efficacy of the proposed architecture, we have presented rigorous and holistic security analysis. To the best of our knowledge, this is the first work that has presented such an in-depth analysis of any deep network in the context of cancelable iris biometrics. Experimental results over four datasets viz. CASIA-V3 Interval, CASIA-Lamp, IITD, and IITK demonstrate the efficacy of the proposed framework in terms of security and accuracy. Further, for better network explainability, we have also performed layer-specific heatmap and feature map analysis to ascertain what exactly our novel deep architecture is learning. Unlabelled Image • Iris Template protection. • Distinctive iris feature extraction based on aggregation learning. • Cancelable iris template generation based on ordinal filtering. • Cancelable iris templates exhaustive security analysis. [ABSTRACT FROM AUTHOR]