Multimodal biometric user authentication using improved decentralized fuzzy vault scheme based on Blockchain network

The fuzzy vault algorithm is mainly used for data privacy protection in fault-tolerant environments, It binds biometric features and secret data together safely to form a vault, which can realize "fuzzy" unlocking. in the real-life, fuzzy vault is mainly used in biometric authentication, image capture, recognition, etc. However, At present, most fuzzy vault schemes have the problems of high computational complexity and low communication efficiency. To solve the problems of high computational complexity and low communication efficiency in identity authentication, we based on the fuzzy vault scheme and construct a multi-secret sharing fuzzy vault scheme. which splits the big secret value into multiple sub-secret values and uses the RS code multi-secret sharing decoding method to improve the efficiency of biological identity authentication. Firstly, splits the big secret value into multiple sub-secret values. Then, We construct a multi-secret sharing scheme. our scheme supports the sharing of multi-secret information. Finally, we analyze the computational complexity and communication complexity of the scheme. The analysis shows that our scheme can reduce the computational complexity and communication complexity by an order of magnitude.

Biometrics based individual identification is observed as an effective technique for automatically knowing, with a high confidence a person's identity. Multi-modal biometric systems consolidate the evidence accessible by multiple biometric sources and normally better recognition performance associate to system based on a single biometric modality.Multi biometric systems are used to overcome this issue by providing multiple pieces of indication of the same identity. This system provides effective fusion structure that combines information provided by the multiple field experts based on decision-level and score-level fusion method, thereby increasing the efficiency which is not conceivable in uni-modal system.Multi-modal biometrics can be attained through a fusion of two or more images, where the subsequent fused image will be more protected. This paper discusses various fusion techniques, architecture of multi-modal biometric authentication and working of biometric fusion i.e. Iris and Fingerprint recognition that are used in multi-modal biometrics.

Based on the classical fuzzy vault and the Diffie-Hellman key exchange scheme, a key sharing fuzzy vault scheme is proposed. In this fuzzy vault scheme, the two users cooperatively build their shared fuzzy vault with a shared key hidden in it using their own biometric features, and they can respectively use their biometric features to unlock the fuzzy vault to get their shared key without running the risk of disclosure of their biometric features later. The security of our scheme is based on the security of the classical fuzzy vault scheme and and the discrete logarithm problem in a given finite group.

The fuzzy vault is an innovative way to share secret keys, combining traditional cryptography with biometrics and biometric template protection. This method forms the basis for the reliable operation of unmanned aerial vehicles (UAVs) through anonymizing drone operators and safely using their data and onboard information. However, due to the inherent instability of biometrics, traditional fuzzy vault schemes face challenges, such as reduced recognition rates with increased chaff points, impractical runtimes due to high-order polynomial reconstruction, and susceptibility to correlation attacks. This paper proposes an efficient fuzzy vault scheme to address these challenges. We generate two secret keys based on biometrics: the first key is produced from the operator’s unique features like the face and iris, using a confidence interval; the second key, used to construct a polynomial, is based on what the operator remembers. These dual-key fuzzy vaults enable the stable generation of genuine points during encoding, easy extraction during decoding, and effective operator authentication while maintaining anonymity. Our experimental results demonstrate improved security and secret acquisition accuracy using the AR face database. These results are achieved regardless of increased false vaults, enabling real-time polynomial reconstruction and resilience against correlation attacks. Importantly, our enhanced fuzzy vault scheme allows the application of this secure, real-time authentication process, safeguarding the anonymity of drone operators.

A novel key sharing fuzzy vault scheme is proposed based on the classic fuzzy vault and the Diffie-Hellman key exchange protocol. In this proposed scheme, two users cooperatively build their fuzzy vault for their shared key using their own biometrics. Either of the users can use their own biometrics to unlock the fuzzy vault with the help of the other to get their shared key without risk of disclosure of their biometrics. Thus, they can unlock the fuzzy vault cooperatively. The security of our scheme is based on the security of the classic fuzzy vault scheme, one-way hash function and the discrete logarithm problem in a given finite group.

We construct an automatic secure fingerprint verification system based on the fuzzy vault scheme to address a major security hole currently existing in most biometric authentication systems. The construction of the fuzzy vault during the enrollment phase is automated by aligning the most reliable reference points between different templates, based on which the converted features are used to form the lock set. The size of the fuzzy vault, the degree of the underlying polynomial, as well as the number of templates needed for reaching the reliable reference point are investigated. This results in a high unlocking complexity for attackers with an acceptable unlocking accuracy for legal users.

Human identification is essential for proper functioning of society. Human identification through multimodal biometrics is becoming an emerging trend, and one of the reasons is to improve recognition accuracy. Unimodal biometric systems are affected by various problemssuch as noisy sensor data,non-universality, lack of individuality, lack of invariant representation and susceptibility to circumvention.A unimodal system has limited accuracy. Hence, Multimodal biometric systems by combining more than one biometric feature in different levels are proposed in order to enhance the performance of the system. A supervisor module combines the different opinions or decisions delivered by each subsystem and then make a final decision. In this paper, a multimodal biometrics authentication is proposed by combining face, iris and finger features. Biometric features are extracted by Local Derivative Ternary Pattern (LDTP) in Contourlet domain and an extensive evaluation of LDTP is done using Support Vector Machine and Nearest Neighborhood Classifier. The experimental evaluations are performed on a public dataset demonstrating the accuracy of the proposed system compared with the existing systems. It is observed that, the combination of face, fingerprint and iris gives better performance in terms of accuracy, False Acceptance Rate, False Rejection Rate with minimum computation time.

The fuzzy vault scheme has recently become popular approaches to biometric template protection. Since the original scheme has been designed to work with unordered biometric features, such a scheme cannot effectively utilize order information. We present in this paper a new fuzzy vault scheme that can effectively utilize the ordered characteristics of biometric features. In this scheme, we develop ordered fuzzy vault encoding and decoding processes in order to utilize the ordered information of the features. This prevents the feature components from cross matching and reduces false acceptance ratio (FAR). Furthermore, the original biometric features (or original template) are transformed into binary features (or secure template) by random transformation. The transformed secure template provides both diversity and revocability. This transform also prevents an adversary from obtaining the original biometric template from the secure template and therefore enhance the secure level of the scheme. Based on the proposed scheme, we design an online authentication application framework implemented using face images. We compare our scheme with two contemporary approaches to verify the effectiveness of this approach. Experimental results show that our scheme is able to achieve an improved performance with several desired properties of an online authentication system.

A crucial issue in the design of a cryptographic system is the problem of key management. A state-of-the-art solution to this problem is to use bio-cryptosystems, in which cryptography is combined with biometrics. In this solution, the user biometrics is used to protect the cryptographic key. A popular approach to the design of such bio-cryptosystems is the application of a fuzzy vault scheme. This so-called vault is a secure storage in which the key is hidden within the biometric data mixed up with meaningless chaff points. The most critical operation in the fuzzy vault scheme is generation of these chaff points. Experiments will show that this module is the most compute-intensive part of the whole system. This paper introduces a new chaff generation algorithm for the fuzzy vault in a bio-cryptosystem. The proposed algorithm, which is based on a circle packing mathematical theorem, is computationally less intensive than existing methods. Experimental results show that the proposed algorithm is around 100 times faster than existing methods for 200, and above, number of chaff points, and therefore, is suitable for a real-time embedded system implementation.

A combination of cryptographic and biometric systems, by performing specific binding technique on cryptographic key and biometric template, the fuzzy vault framework enhances the security level of current biometric cryptographic systems in terms of hiding secret key and protecting the template. Although the original scheme suggests the use of error-correction techniques (e.g. the Reed–Solomon code) to reconstruct the original polynomial, recent implementations do not share the same point of view. Instead, cyclic redundant code (CRC) is applied to identify the genuine polynomial from a set of candidates due to its simplicity. Within the scope of this study, the authors address a significant flaw of current CRC-based fuzzy vault schemes, which allows the potential of successful blend substitution attack. To overcome this problem, an integration of two novel modules into general fuzzy vault scheme, namely chaff point generator and verifier, is proposed. The new modules are designed to be integrated easily into the existing systems as well as simple to enhance. The proposed scheme can detect any modification in vault and, as a result, eliminate the blend substitution attack to improve general security. Moreover, the experimental results of this study with real-world datasets show an increase in genuine acceptance rates.

Biometric cryptosystems have been applied to secure secret keys for encryption and digital signatures by means of biometric traits, e.g., fingerprint, face, etc., where the fuzzy vault (FV) mechanism has been extensively employed. Recently, the authors proposed a FV system based on the offline signature images, so that digitized documents can be secured with the embedded handwritten signatures. However, the FV design concerns mostly with alleviating biometric variability with less focusing on its power in discriminating forgeries. Accordingly, the decoding accuracy of implementations is below the level required in practical banking transactions. On the other hand, signature verification (SV) systems have shown higher accuracy in discriminating forgeries. In this paper, accuracy of signaturebased biometric cryptosystems is enhanced by cascading SV and FV modules. Signature samples are first verified by the SV module. Then, only verified samples are processed by FV decoders for unlocking cryptographic keys. Hence, the upper limit of the false accept rate is determined by the more accurate SV module. Simulation results obtained with the Brazilian signature database indicate the viability of the proposed approach. Cascaded SV-FV system increases decoding accuracy by about 35% compared to the pure FV systems.

As a consequence of the wide deployment of biometrics-based recognition systems, there are increasing concerns about the security of the sensitive information managed. Various techniques have been proposed in the literature for the biometric templates protection (BTP), having gained great popularity the crypto-biometric systems. In the present paper we propose the implementation of a Fuzzy Vault (FV) scheme based on fixed-length templates with application to dynamic signature verification (DSV), where only 15 global features of the signature are considered to form the templates. The performance of the proposed system is evaluated using three databases: a proprietary collection of signatures, and the publicly available databases MCYT and BioSecure. The experimental results show very similar verification performance compared to an equivalent unprotected system.

In this paper, an improved reversible data hiding approach based on correlation analysis for multimodal biometric identification is proposed to ensure the security and integrity of multimodal biometric images. Considering the characteristics of biometrics, correlation analysis is first utilized between the face image and the palm image to enhance the security, and a residual image is obtained by exploiting the spatial and content correlation. Then an improved method is presented to embed the residual palm image into face image for secure transmission. Finally, image authentication is performed for further multimodal biometric identification at the receiver. Once images are authentic, identification is carried out for further identity authentication. Experimental results demonstrate that our proposed method has better quality than the previous work and the perfect security performance is validated on biometric identification.

The convergence of blockchain, artificial intelligence (AI), and cloud computing is catalyzing a paradigm shift in developing secure, intelligent, and scalable digital infrastructures. This triad of technologies is increasingly utilized to improve performance, transparency, and trust in engineering-driven and socio-technical environments. This study systematically reviews the evolution, integration strategies, and applications of blockchain, AI, and cloud computing in digital ecosystems. The analysis is based on 108 peer-reviewed studies spanning the years 2012 to 2025. A comprehensive literature analysis was conducted to identify trends, synergies, and sector-specific implementations of these systems. The review explores how their integration supports real-world engineering and operational use cases. Blockchain contributes to decentralized architectures, secure data exchange, and identity verification. AI supports adaptive behavior, autonomous decision-making, and predictive analytics. Cloud computing offers the scalable infrastructure necessary for deployment. Key challenges addressed include interoperability, latency, security trade-offs, and resource allocation. Use cases in digital finance, supply chain management, and industrial automation demonstrate the effectiveness of this integration in building resilient, ethically aligned, and high-performance infrastructures. The findings offer valuable insights and technical considerations for engineers and architects seeking to design next-generation cyber-physical systems that are secure, intelligent, and socially responsive. Clinical Trial Number Not applicable. This article accentuates the extensive use of blockchain, cloud computing, and AI principles, emphasizing their crucial significance in the contemporary digital environment and paths in which blockchain technology may be employed to automate financial transactions, enhance the security of personal data and records, facilitate cross-border transactions, verify identities, execute smart contracts, utilize cryptocurrencies, and manage supply chains. This research systematically assesses the development and practical application of blockchain, cloud computing, and artificial intelligence technologies over the past decade. This study primarily focuses on monitoring the progress, practical applications, and benefits of blockchain technology in the context of financial transactions. This article probes the symbiotic relationship between artificial intelligence, cloud and blockchain technology, analyzing the evolution and fundamental concepts of their integration from 2012 to 2025. This paper also presents a detailed analysis of 108 research works acquitted over the past decade. The study provides a comprehensive examination of the current state and fundamental principles related to the integration of artificial intelligence, cloud, and blockchain technology. This paper offers a comprehensive analysis of the major obstacles confronted by blockchain technology, examining its wide-ranging applications over several industries and its socko role in transaction systems and the domain of cryptocurrencies.

Theft of a template is theft of identity. Fingerprint identification is used by forensic department in identifying criminals because of its uniqueness. Biometrics is the science of verifying and establishing the identity of an individual through physiological features or behavioral traits. Fingerprint identification is commonly employed in Forensic science to support criminal investigations and in biometric systems such as civilian and commercial identification devices. The best way to identify criminals is using fingerprint verification which cannot be replaced. So such fingerprint acquired from the criminal should be safely stored and protected from hackers for future references. This paper mainly deals with protecting the criminal's record (fingerprint template). Templates represent intrinsic information about a criminal. The criminal record (Fingerprint template) should be protected from creation of physical spoof and replacement by imposter's template to gain unauthorized access. Due to these reasons, fool-proof techniques are required for securely storing the criminal fingerprint records. Fuzzy vault is a cryptographic construct that is designed to work with biometric features represented as an unordered set like minutiae points. Template protection is done by transformation based approaches and biometric cryptosystems. During transformation template is stored in a transformed form so that the original biometric template cannot be easily recovered. Fingerprint from the criminal is first preprocessed and minutiae points are extracted. Preprocessing is very essential because the creation of fuzzy vault highly depends on the quality of the input fingerprint image. Then image is binarized followed by thinning and then region of interest (ROI) is calculated finally followed by removal of false minutiae points. The remaining useful minutiae points are used in fuzzy vault generation. A polynomial is evaluated from the secret key which is combined with minutiae points to produce a secure vault. Some dummy points called chaff points are also added to increase the security. The security of the fuzzy vault depends on the infeasibility of the polynomial reconstruction and the number of chaff points. In the proposed system an even more secured fuzzy vault is generated with combined features of fingerprint and palm print to enhance the security of the template stored.