Abstract With the the advent era of big data, the secure computation calculates data on the premise of protecting data privacy, to realize the availability and invisibility of data. Secure multi-party computation, as one of three major technical tools of privacy computing, can still securely carry out data collaborative computation without a trusted third party. As an important branch of secure multi-party computation, the secure computing geometric problem can solve practical problems in the military, national defense, finance, life, and other fields, which has important research significance. In this paper, the graphic similarity problem is studied. Firstly, this paper proposes the adjacency matrix vector coding method of isomorphic graphics and uses the Paillier variant cryptosystem to securely solve the graphic similarity judgment under the semi-honest model. By using an elliptic curve cryptosystem and zero-knowledge proof to solve the possible malicious attacks under the semi-honest model, a graphic similarity judgment protocol under the malicious model is designed. The protocol can resist malicious attacks, has high computational efficiency, and has wide application value.
Non-fungible tokens (NFTs) are becoming very popular in a large number of applications ranging from copyright protection to monetization of both physical and digital assets. It is however a fact that NFTs suffer from a large number of security issues that create a lack of trust in solutions based on them. In this paper, we provide an overview of some of the most critical security challenges in media assets in form of visual content and then propose a specific solution for one among them, namely, secure person identification used in the context of KnowYour-Customer (KYC) with emphasis on liveness detection. The solution includes an authentication procedure that matches a selfie photo to a photograph of an identity document (ID). The system runs through a series of steps. First, detection is applied to extract faces from the selfie and the ID. Then a face comparison is performed to assess if they belong to the same person. While these two procedures are standard in KYC, a liveness check is also included so as to increase the security. The latter ensures that the user undergoing identity verification is in front of the camera and not a fraudster attempting to impersonate another individual. The system instructs the user to perform gestures such as waving hands or tilting head in front of the camera. The algorithmic detection of these actions during the live feed will reveal whether or not the user is carrying out the instructed activities. Performance of the proposed solution is then assessed under varying conditions.
Mehedi Masud, Gurjot Singh Gaba, Pardeep Kumar, Andrei Gurtov
Ambient Intelligence (AmI) in Internet of Things (IoT) has empowered healthcare professionals to monitor, diagnose, and treat patients remotely. Besides, the AmI-IoT has improved patient engagement and gratification as doctors’ interactions have become more comfortable and efficient. However, the benefits of the AmI-IoT-based healthcare applications are not availed entirely due to the adversarial threats. IoT networks are prone to cyber attacks due to vulnerable wireless mediums and the absentia of lightweight and robust security protocols. This paper introduces computationally-inexpensive privacy-assuring authentication protocol for AmI-IoT healthcare applications. The use of blockchain & fog computing in the protocol guarantees unforgeability, non-repudiation, transparency, low latency, and efficient bandwidth utilization. The protocol uses physically unclonable functions (PUF), biometrics, and Ethereum powered smart contracts to prevent replay, impersonation, and cloning attacks. Results prove the resource efficiency of the protocol as the smart contract incurs very minimal gas and transaction fees. The Scyther results validate the robustness of the proposed protocol against cyber-attacks. The protocol applies lightweight cryptography primitives (Hash, PUF) instead of conventional public-key cryptography and scalar multiplications. Consequently, the proposed protocol is better than centralized infrastructure-based authentication approaches.
Biometric identification is a convenient and reliable method in identity authentication. The widespread adoption of biometric identification requires strong privacy protection against possible theft or loss of biometric data. Existing techniques for privacy-preserving biometric identification mainly rely on traditional cryptographic technology such as oblivious transfer and homomorphic encryption, which will incur huge expenses to the system and cannot be applied to large-scale practical applications. For these issues, we propose a biometric identification scheme by constructing zero-knowledge succinct noninteractive argument of knowledge (zk-SNARK). Our scheme not only reduces the communication overhead, which only needs to send 8 constants to the verifier but also can protect the fingerprint template from disclosure. The time complexity of proof generation and proof verification are about O(C) and <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>O</a:mi> <a:mfenced open="(" close=")" separators="|"> <a:mrow> <a:mi>x</a:mi> </a:mrow> </a:mfenced> <a:mo>+</a:mo> <a:mi mathvariant="normal">log</a:mi> <a:mtext> </a:mtext> <a:mtext> </a:mtext> <a:mi>C</a:mi> </a:math> , respectively, and the size of the proof is only 8 constants, where C and x represent the size of the circuit and the public input, respectively. We have implemented the proposed authentication solution on a public data set of fingerprint images and evaluated the performance and security.
Yangxu Song, Frank Jiang, Syed Wajid Ali Shah, Robin Doss
With the development of 5G networking technology on the Internet of Vehicle (IoV), there are new opportunities for numerous cyber-attacks, such as in-vehicle attacks like hijacking occurrences and data theft. While numerous attempts have been made to protect against the potential attacks, there are still many unsolved problems such as developing a fine-grained access control system. This is reflected by the granularity of security as well as the related data that are hosted on these platforms. Among the most notable trends is the increased usage of smart devices, IoV, cloud services, emerging technologies aim at accessing, storing and processing data. Most popular authentication protocols rely on knowledge-factor for authentication that is infamously known to be vulnerable to subversions. Recently, the zero-trust framework has drawn huge attention; there is an urgent need to develop further the existing Continuous Authentication (CA) technique to achieve the zero-trustiness framework. In this paper, firstly, we develop the static authentication process and propose a secured protocol to generate the smart key for user to unlock the vehicle. Then, we proposed a novel and secure continuous authentication system for IoVs. We present the proof-of-concept of our CA scheme by building a prototype that leverages the commodity fingerprint sensors, NFC, and smartphone. Our evaluations in real-world settings demonstrate the appropriateness of CA scheme and security analysis of our proposed protocol for digital key suggests its enhanced security against the known attack-vector.
During the COVID-19 pandemic, engagement in various remote activities such as online education and meetings has increased. However, since the conventional online environments typically provide simple streaming services using cameras and microphones, there have limitations in terms of physical expression and experiencing real-world activities such as cultural and economic activities. Recently, metaverse environments, three-dimensional virtual reality that use avatars, have attracted increasing attention as a means to solve these problems. Thus, many metaverse platforms such as Roblox, Minecraft, and Fortnite have been emerging to provide various services to users. However, such metaverse environments are potentially vulnerable to various security threats because the users and platform servers communicate through public channels. In addition, sensitive user data such as identity, password, and biometric information are managed by each platform server. In this paper, we design a system model that can guarantee secure communication and transparently manage user identification data in metaverse environments using blockchain technology. We also propose a mutual authentication scheme using biometric information and Elliptic Curve Cryptography (ECC) to provide secure communication between users and platform servers and secure avatar interactions between avatars and avatars. To demonstrate the security of the proposed mutual authentication scheme, we perform informal security analysis, Burrows–Abadi–Needham (BAN) logic, Real-or-Random (ROR) model, and Automated Validation of Internet Security Protocols and Applications (AVISPA). In addition, we compare the computation costs, communication costs, and security features of the proposed scheme with existing schemes in similar environments. The results demonstrate that the proposed scheme has lower computation and communication costs and can provide a wider range of security features than existing schemes. Thus, our proposed scheme can be used to provide secure metaverse environments.
Quang Nhat Tran, Benjamin Turnbull, Min Wang, Jiankun Hu
Biometric authentication is, over time, becoming an indispensable complementary component to traditional authentication methods that use passwords and tokens. As a result, the research interest in the protection techniques for the biometric template has also grown considerably. In this paper, we present a light-weight AI-based biometric authentication that operates based on the binary representation of a biometric instance. In details, a binary classifier will be trained using the binary strings that represent the intraclass and interclass biometric subjects. The Support Vector Machine and Multi-layer Perceptron Neural Network are chosen as the classifier to evaluate the fingerprint-based and iris-based authentication capability. Afterward, the authenticated biometric string is fed to a hash function to produce a hash value, which is to be used in a Zero-Knowledge-Proof Protocol for the purpose of privacy preservation. In order to improve the recognition of the classifier, we devise a simple yet efficient strategy to enhance the discriminativeness of the binary strings and name it the Composite Features Retrieval. We evaluated the proposed method with the four publicly available fingerprint datasets FVC2002-DB1, FVC2002-DB2, FVC2002-DB3, and FVC2004-DB2 and the iris dataset UBIRISv1. The promising performance shows this method's capability.
Xuechun Mao, Lin You, Chengtang Cao, Gengran Hu · 5 authors
Biometric encryption, especially based on fingerprint, plays an important role in privacy protection and identity authentication. In this paper, we construct a privacy-preserving linkable ring signature scheme. In our scheme, we utilize a fuzzy symmetric encryption scheme called symmetric keyring encryption (SKE) to hide the secret key and use non-interactive zero-knowledge (NIZK) protocol to ensure that we do not leak any information about the message. Unlike the blind signature, we use NIZK protocol to cancel the interaction between the signer (the prover) and the verifier. The security proof shows that our scheme is secure under the random oracle model. Finally, we implement it on a personal computer and analyze the performance of the constructed scheme in practical terms. Based on the constructed scheme and demo, we give an anonymous cryptocurrency transaction model as well as mobile demonstration.
Zero-knowledge proof (ZKP) is a fundamental cryptographic primitive that allows a prover to convince a verifier of the validity of a statement without leaking any further information. As an efficient variant of ZKP, noninteractive zero-knowledge proof (NIZKP) adopting the Fiat-Shamir heuristic is essential to a wide spectrum of applications, such as federated learning, blockchain, and social networks. However, the heuristic is typically built upon the random oracle model that makes ideal assumptions about hash functions, which does not hold in reality and thus undermines the security of the protocol. Here, we present a quantum solution to the problem. Instead of resorting to a random oracle model, we implement a quantum randomness service. This service generates random numbers certified by the loophole-free Bell test and delivers them with postquantum cryptography (PQC) authentication. By employing this service, we conceive and implement NIZKP of the three-coloring problem. By bridging together three prominent research themes, quantum nonlocality, PQC, and ZKP, we anticipate this work to inspire more innovative applications that combine quantum information science and the cryptography field.
E-learning has been carried out all over the world and then online examinations have become an important means to check learning effect during the outbreak of COVID-19. Participant authenticity, data integrity, and access control are the assurance to online examination. The existing online examination schemes cannot provide the protection of biometric features and fine-grained access control. Particularly, they did not discuss how to resolve some disputes among students, teachers, and a platform in a fair and reasonable way. We propose a novel biometric authentication and blockchain-based online examination scheme. The examination data are encrypted to store in a distributed system, which can be obtained only if the user satisfies decryption policy. And the pieces of evidence are recorded in a blockchain network which is jointly established by some credible institutions. Unlike other examination authentication systems, face templates in our scheme are protected using a fuzzy vault and a cryptographic method. Furthermore, educational administrative department can determine who the real initiator of malicious behavior is when a dispute arises using a dispute determination protocol. Analysis shows that no central authority is required in our scheme; the collusion of multiple users cannot obtain more data; even if the authorities compromise, biometric features of each user will not be leaked. Therefore, in terms of privacy-preserving biometric templates, fine-grained access, and dispute resolution, it is superior to the existing schemes.
Open access
Biometric Identification and Security
Advanced Steganography and Watermarking Techniques
Biometric authentication systems have major security weaknesses: risk of biometric information leakage, unreliability of authentication modules, and non-transparency of biometric information management. This paper presents BDAS, a new biometric authentication system using blockchain, which provides a decentralized and distributed mechanism for processing biometric authentication and an auditable mechanism for managing biometric information. BDAS’s evaluation demonstrates that it provides reliable and secure authentication compared to existing methods while introducing negligible performance overhead in real-world scenarios.
Biometric verification has been widely deployed in current authentication solutions as it proves the physical presence of individuals. To protect the sensitive biometric data in such systems, several solutions have been developed that provide security against honest-but-curious (semi-honest) attackers. However, in practice attackers typically do not act honestly and multiple studies have shown drastic biometric information leakage in such honest-but-curious solutions when considering dishonest, malicious attackers. In this paper, we propose a provably secure biometric verification protocol to withstand malicious attackers and prevent biometric data from any sort of leakage. The proposed protocol is based on a homomorphically encrypted log likelihood-ratio-based (HELR) classifier that supports any biometric modality (e.g. face, fingerprint, dynamic signature, etc.) encoded as a fixed-length real-valued feature vector and performs an accurate and fast biometric recognition. Our protocol, that is secure against malicious adversaries, is designed from a protocol secure against semi-honest adversaries enhanced by zero-knowledge proofs. We evaluate both protocols for various security levels and record a sub-second speed (between $0.37$s and $0.88$s) for the protocol against semi-honest adversaries and between $0.95$s and $2.50$s for the protocol secure against malicious adversaries.
Bitcoin transactions rely on digital signatures to prove the ownership of bitcoin. The private signing key of the bitcoin owner is the key component to enable a bitcoin transaction. If the signing key of a bitcoin is stolen, the theft who possesses the key can make a transaction of the bitcoin. In this paper, based on the distance-based encryption (DBE), we propose an enhanced version of bitcoin in order to protect the signing key. Our approach is based on our two-factor authentication, where the signing key cannot be retrieved without being identified via the password and biometric authentication scheme, and the user is only required to enter his password and fingerprint (or other biometric information such as a factual image) to retrieve the key. By doing this, we can effectively improve the bitcoin security and provide stronger authentication. An attractive feature of our scheme is that one of encryption schemes is asymmetric, in the sense that the decryption key (biometric information) is not stored in the device. We also provide the security model and proof to justify the security of our scheme.
Open access
2 source records
Blockchain Technology Applications and Security
User Authentication and Security Systems
Advanced Steganography and Watermarking Techniques
Ming Li, Leilei Zeng, Le Zhao, Renlin Yang · 6 authors
With the application of multimedia big data, the problems such as information leakage and data tampering have emerged. The security of images which is one of the most typical multimedia has become a major problem facing the large-scale open network environment. This paper proposed a blockchain-watermarking scheme to protect the privacy, integrity and availability of compressed sensed images, which effectively combines multimedia watermarking, compressed sensing, Interplanetary File System (IPFS) and blockchain technologies. Based on the reliable authentication of watermarking, the confidentiality protection of compressed sensing, the secure storage of IPFS, and the decentralization and non-tamperability of blockchain, the all-round security protection of the image big data based on compressive sensing can be realized. Experiments show that the proposed scheme is effective and feasible.
Open access
Advanced Steganography and Watermarking Techniques
Image authentication is an important field that employs many different approaches and has several significant applications. In the proposed approach, we used a combination of two techniques to achieve authentication. Image watermarking is one of the techniques that has been used in many studies but the authentication field still needs to be studied. Blockchain technology is a relatively new technology that has significant research potential related to image authentication. The watermark is embedded into the third-level discrete wavelet transform (DWT) in the middle frequency regions to achieve security and imperceptibility goals. Peak signal-to-noise ratio PSNR, structural similarity matrix (SSIM), normalized correlation coefficient (NCC), and bit error rate (BER) are used to measure the performance of image watermarking. We used blockchain technology to avoid involving a trusted third party for authentication. Secure Hash Algorithm 256 (SHA-256) is applied on the watermark to save it into the blockchain. The watermark is encrypted using Advanced Encryption Standard (AES) and embedded into the image. The proposed method is tested on the USP SICI database and the MedPix medical image database. Ethereum blockchain is used to provide security, anonymity, and integrity of data with no third-party intervention. The proposed solution demonstrates enhanced security for image authentication compared with the state-of-the-art.
Open access
Advanced Steganography and Watermarking Techniques
Biometric verification has been widely deployed in current authentication solutions as it proves the physical presence of individuals. Several solutions have been developed to protect the sensitive biometric data in such systems that provide security against honest-but-curious (a.k.a. semi-honest) attackers. However, in practice, attackers typically do not act honestly and multiple studies have shown severe biometric information leakage in such honest-but-curious solutions when considering dishonest, malicious attackers. In this paper, we propose a provably secure biometric verification protocol to withstand malicious attackers and prevent biometric data from any leakage. The proposed protocol is based on a homomorphically encrypted log likelihood-ratio (HELR) classifier that supports any biometric modality (e.g., face, fingerprint, dynamic signature, etc.) encoded as a fixed-length real-valued feature vector. The HELR classifier performs an accurate and fast biometric recognition. Furthermore, our protocol, which is secure against malicious adversaries, is designed from a protocol secure against semi-honest adversaries enhanced by zero-knowledge proofs. We evaluate both protocols for various security levels and record a sub-second speed (between 0.37s and 0.88s) for the protocol secure against semi-honest adversaries and between 0.95s and 2.50s for the protocol secure against malicious adversaries.
Sasha Shilina, Dato Kavazi, Victor Smirnov, Viktor Smirnov · 9 authors
The advent of blockchain technology has led to a massive wave of different decentralized ledger technology (DLT) solutions. Such projects as Bitcoin and Ethereum have shifted the paradigm of how to transact value in a decentralized manner, but their various core technologies have their own advantages and disadvantages. This paper aims to describe an alternative to modern decentralized financial networks by introducing the Humanode network. Humanode is a network safeguarded by cryptographically secure bio-authorized nodes. Users will be able to deploy nodes by staking their encrypted biometric data. This approach can potentially lead to the creation of a public, permissionless financial network based on consensus between equal human nodes with algorithm-based emission mechanisms targeting real value growth and proportional emission. Humanode combines different technological stacks to achieve a decentralized, secure, scalable, efficient, consistent, immutable, and sustainable financial network: 1) a bio-authorization module based on cryptographically secure neural networks for the private classification of 3D templates of users' faces 2) a private Liveness detection mechanism for identification of real human beings 3) a Substrate module as a blockchain layer 4) a cost-based fee system 5) a Vortex decentralized autonomous organization (DAO) governing system 6) a monetary policy and algorithm, Fath, where monetary supply reacts to real value growth and emission is proportional. All of these implemented technologies have nuances that are crucial for the integrity of the network. In this paper we address these details, describing problems that might occur and their possible solutions. The main goal of Humanode is to create a stable and just financial network that relies on the existence of human life.
Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses. Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially.
Oday Ali Hassen, Ansam A. Abdulhussien, Saad M. Darwish, Zulaiha Ali Othman · 6 authors
Blockchain technology has been commonly used in the last years in numerous fields, such as transactions documenting and monitoring real assets (house, cash) or intangible assets (copyright, intellectual property). The internet of things (IoT) technology, on the other hand, has become the main driver of the fourth industrial revolution, and is currently utilized in diverse fields of industry. New approaches have been established through improving the authentication methods in the blockchain to address the constraints of scalability and protection in IoT operating environments of distributed blockchain technology by control of a private key. However, these authentication mechanisms do not consider security when applying IoT to the network, as the nature of IoT communication with numerous entities all the time in various locations increases security risks resulting in extreme asset damage. This posed many difficulties in finding harmony between security and scalability. To address this gap, the work suggested in this paper adapts multimodal biometrics to strengthen network security by extracting a private key with high entropy. Additionally, via a whitelist, the suggested scheme evaluates the security score for the IoT system with a blockchain smart contract to guarantee that highly secured applications authenticate easily and restrict compromised devices. Experimental results indicate that our system is existentially unforgeable to an efficient message attack, and therefore, decreases the expansion of infected devices to the network by up to 49 percent relative to traditional schemes.
The Internet of Things (IoT) is built on a strong internet infrastructure and many wireless sensor devices. Presently, Radio Frequency Identification embedded (RFID-embedded) smart cards are ubiquitous, used for many things including student ID cards, transportation cards, bank cards, prepaid cards, and citizenship cards. One example of places that require smart cards is libraries. Each library, such as a university library, city library, local library, or community library, has its own card and the user must bring the appropriate card to enter a library and borrow material. However, it is inconvenient to bring various cards to access different libraries. Wireless infrastructure has been well developed and IoT devices are connected through this infrastructure. Moreover, the development of biometric identification technologies has continued to advance. Blockchain methodologies have been successfully adopted in various fields. This paper proposes the BlockMetrics library based on integrated technologies using blockchain and finger-vein biometrics, which are adopted into a library collection management and access control system. The library collection is managed by image recognition, RFID, and wireless sensor technologies. In addition, a biometric system is connected to a library collection control system, enabling the borrowing procedure to consist of only two steps. First, the user adopts a biometric recognition device for user authentication and then performs a collection scan with the RFID devices. All the records are recorded in a personal borrowing blockchain, which is a peer-to-peer transfer system and permanent data storage. In addition, the user can check the status of his collection across various libraries in his personal borrowing blockchain. The BlockMetrics library is based on an integration of technologies that include blockchain, biometrics, and wireless sensor technologies to improve the smart library.
Sarang Chaudhari, Michael Clear, Philip Bradish, Hitesh Tewari
Uniquely identifying individuals across the various networks they interact with on a daily basis remains a challenge for the digital world that we live in, and therefore the development of secure and efficient privacy preserving identity mechanisms has become an important field of research. In addition, the popularity of decentralised decision making networks such as Bitcoin has seen a huge interest in making use of distributed ledger technology to store and securely disseminate end user identity credentials. In this paper we describe a mechanism that allows one to store the COVID-19 vaccination details of individuals on a publicly readable, decentralised, immutable blockchain, and makes use of a two-factor authentication system that employs biometric cryptographic hashing techniques to generate a unique identifier for each user. Our main contribution is the employment of a provably secure input-hiding, locality-sensitive hashing algorithm over an iris extraction technique, that can be used to authenticate users and anonymously locate vaccination records on the blockchain, without leaking any personally identifiable information to the blockchain.
Moses Arhinful Acquah, Na Chen, Jeng‐Shyang Pan, Hong-Mei Yang · 5 authors
Biometrics, with its uniqueness to every individual, has been adapted as a security authentication feature by many institutions. These biometric data are processed into templates that are saved on databases, and a central authority centralizes and controls these databases. This form of storing biometric data, or in our case fingerprint template, is asymmetric and prone to three main security attacks, such as fake template input, template modification or deletion, and channel interception by a malicious attacker. In this paper, we secure an encrypted fingerprint template by a symmetric peer-to-peer network and symmetric encryption. The fingerprint is encrypted by the symmetric key algorithm: Advanced Encryption Standard (AES) algorithm and then is uploaded to a symmetrically distributed storage system, the InterPlanetary File system (IPFS). The hash of the templated is stored in a decentralized blockchain. The slow transaction speed of the blockchain has limited its use in real-life applications, such as large file storage, hence, the merge with IPFS to store just the hashes of large files. The encrypted template is uploaded to the IPFS, and its returned digest is stored on the Ethereum network. The implementation of IPFS prevents storing the raw state of the fingerprint template on the Ethereum network in order to reduce cost and also prevent identity theft. This procedure is an improvement of previous systems. By adopting the method of template hashing, the proposed system is cost-effective and efficient. The experimental results depict that the proposed system secures the fingerprint template by encryption, hashing, and decentralization.
Open access
Biometric Identification and Security
Advanced Steganography and Watermarking Techniques
Morampudi Mahesh Kumar, Munaga V. N. K. Prasad, U. S. N. Raju
Multi‐biometric systems have been widely accepted in various applications due to its capability to solve the limitations of unimodal systems. Directly storing the biometric templates into a centralised server leads to privacy concerns. In the past few years, many biometric authentication systems based on homomorphic encryption have been introduced to provide security for the templates. Most of the existing solutions rely on an implication of the assumption that the server is ‘honest‐but‐curious’. Therefore, the compromise of server results into the entire system vulnerability and fails to provide the integrity. To address this, we propose a novel multi‐instance iris authentication system, BMIAE to deal with malicious attacks over the transmission channel and at the untrusted server. BMIAE encrypt the iris templates using ElGamal encryption to guarantee confidentiality and Smart contract running on a Blockchain helps to achieve the integrity of templates and matching result. BMIAE also addresses the limitations of using Blockchain for biometrics like privacy and expensive storage. To check the effectiveness and robustness, BMIAE has experimented on CASIA‐V3‐Interval, IITD and SDUMLA‐HMT iris databases. Experimental results show that BMIAE provides improved accuracy, and eliminates the need to trust the centralised server when compared to the state‐of‐the‐art approaches.
Open access
Biometric Identification and Security
User Authentication and Security Systems
Advanced Steganography and Watermarking Techniques