Decentralized identity systems promise user-controlled identifiers and cross-domain verification without a shared identity provider, yet authentication still reduces to possession of keys or credentials once secrets are leaked, reused, or replayed. We present BioZero, a privacy-preserving biometric authentication protocol for decentralized identity that binds an enrolled identity to a biometric witness without revealing biometric templates, while enabling publicly verifiable on-chain decisions. BioZero combines Pedersen commitment-homomorphic computation, consistency spot-checks, and Groth16 zero-knowledge proofs to achieve identity-bound authentication with succinct on-chain verification. We analyze acceptance soundness, freshness, template privacy, and non-malleability under an open decentralized threat model including replay, timing, brute-force, oracle, and forgery attacks. On an Ethereum testbed, BioZero achieves up to 67.8x lower network-adjusted total authentication latency and up to 266.4x faster client-side proving than a zk-SNARK-only baseline. Verification stays in the millisecond range (28.8-41.2 ms vs. 35.4-77.6 ms). With lambda=1 spot-checking, gas grows from 336,778 to 954,066 as N increases from 2 to 128, becomes lower than the baseline from N>=16, and is 2.59x lower at N=128. LFW experiments on 128D and 512D models show accuracy loss below 1% across practical quantization ranges. These results indicate that BioZero is a practical authentication layer for decentralized biometric identity systems.
The increased use of artificial intelligence generated content (AIGC) among vast user populations has heightened the risk of private data leaks. Effective auditing and regulation remain challenging, further compounding the risks associated with the leaks involving model parameters and user data. Blockchain technology, renowned for its decentralized consensus mechanism and tamper-resistant properties, is emerging as an ideal tool for documenting, auditing, and analyzing the behaviors of all stakeholders in machine learning as a service (MLaaS). This study centers on biometric recognition systems, addressing pressing privacy and security concerns through innovative endeavors. We conducted experiments to analyze six distinct deep neural networks, leveraging a dataset quality metric grounded in the query output space to quantify the value of the transfer datasets. This analysis revealed the impact of imbalanced datasets on training accuracy, thereby bolstering the system's capacity to detect model data thefts. Furthermore, we designed and implemented a novel Bio-Rollup scheme, seamlessly integrating technologies such as certificate authority, blockchain layer two scaling, and zero-knowledge proofs. This innovative scheme facilitates lightweight auditing through Merkle proofs, enhancing efficiency while minimizing blockchain storage requirements. Compared to the baseline approach, Bio-Rollup restores the integrity of the biometric system and simplifies deployment procedures. It effectively prevents unauthorized use through certificate authorization and zero-knowledge proofs, thus safeguarding user privacy and offering a passive defense against model stealing attacks.
Alec Wells, Norbert Dajnowski, Aminu Bello Usman, John Murray · 5 authors
This paper presents a novel framework for one-to-many biometric systems by adapting decentralised storage over a centralised database solution, by leveraging smart contracts to address the concerns commonly associated with decentralised solutions. Smart contracts enforce strict privacy controls, enabling individuals to retain ownership and control over their biometric data on decentralised networks, while facilitating secure and efficient authentication, helping achieve the principles laid out by privacy by design. Biometric systems play a crucial role in identity verification and access control, but their deployment raises significant privacy challenges due to the sensitive nature of biometric data. Traditional approaches often involve centralised storage of biometric information, increasing the risk of data breaches and unauthorised access. We discuss the architecture, implementation, and benefits of our framework, highlighting its potential to enhance privacy and trust in one-to-many biometric systems across various applications.
Samuel Hand, Alexander Koch, Pascal Lafourcade, Daiki Miyahara · 5 authors
Abstract A zero-knowledge proof (ZKP) allows a prover to prove to a verifier that it knows some secret, such as a solution to a difficult puzzle, without revealing any information about it. In recent years, ZKP protocols using only a deck of playing cards for solutions to various pencil puzzles have been proposed. The previous work of Lafourcade et al. deals with a famous puzzle called Slitherlink. Their proposed protocol can verify that a solution forms a single loop without revealing anything about the solution, except this fact. Their protocol guarantees that the solution satisfies the single-loop condition, by interactively constructing a solution starting from a state that holds a simple single loop, and proceeding via steps that preserve the invariant of encoding a single loop, until the proper solution is reached. A drawback of their protocol is that it requires additional verifications to guarantee a single loop. In this study, we propose a more efficient ZKP protocol for such a puzzle with fewer additional verifications. For this, we employ the previous work of Robert et al., which addressed the connectivity property in a puzzle. That is, we verify that a solution is connected but not split, to be a single loop. Applying our proposal, we construct a card-based ZKP protocol for Moon-or-Sun, which has its specific rule of alternating pattern in addition to the single-loop condition.
Open access
Advanced Steganography and Watermarking Techniques
Chunjie Guo, Lin You, Xingyu Li, Gengran Hu · 6 authors
Biometric authentication is a very convenient and user-friendly method. The popularity of this method requires strong privacy-preserving technology to prevent the disclosure of template information. Most of the existing privacy protection technologies rely on classic encryption techniques, such as homomorphic encryption, which incur huge system overhead and cannot be popularized. To address these issues, we propose a novel biometric authentication scheme with privacy protection based on support vector machine and zero knowledge proof (BioAu–SVM+ZKP). BioAu–SVM+ZKP allows users to authenticate themselves to different service providers without disclosing any biometric template information. The evidence is generated through the zero-knowledge proof utilizing polynomial commitments. Our approach for generating a unique and repeatable biometric identifier from the user’s fingerprint image leverages the multi-classification property of SVM. Notably, our scheme not only reduces the communication overhead but also provides the privacy protection features. Besides, the communication overhead of BioAu–SVM+ZKP is constant. We have simulated the authentication scheme on the common dataset NIST, analyzed the performance and proved the security.
Open access
Biometric Identification and Security
User Authentication and Security Systems
Advanced Steganography and Watermarking Techniques
Biometric authentication has been used in applications in various environments as a secure authentication method in computing systems.When combined with blockchain technology, the security of the biometric authentication system can be further enhanced.In this paper, we propose a biometric authentication system that does not expose the original biometric information during the user's biometric authentication process by utilizing a fully homomorphic encryption.In addition, our proposed authentication system utilizes Ethereum's smart contract, which is one of the most famous public blockchains, to record the authentication log between the user and the service provider in a distributed ledger to enhance accountability and traceability.The system is designed to be used only after obtaining the consent of the biometric data subject(user) to comply with the privacy law represented by the European General Data Protection Regulation (GDPR).Finally, we show that the proposed system can process biometric information while maintaining confidentiality, integrity, and accountability of users via security analysis.The cost of maintaining the proposed system is acceptable by analyzing computation time and blockchain maintenance cost.
Saeed Bamashmos, Naveen Chilamkurti, Ahmad Salehi Shahraki
Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie-Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency.
Open access
User Authentication and Security Systems
Biometric Identification and Security
Advanced Steganography and Watermarking Techniques
Teng Cheng, Qiang Liu, Qin Shi, Ze Yang · 7 authors
Near-field communication in VANETs can effectively reduce communication overhead compared to peer-to-peer communication. However, there is still plenty of room for improvements to be made to ensure identity authentication privacy protection and to enhance the security and efficiency of key distributions during transmissions. Therefore, this paper proposes an anonymous identity authentication and group key distribution scheme based on quantum random numbers. In the proposed scheme, (1) anonymous credentials for vehicles are generated by a combination of random numbers on the vehicle side and random numbers in the TA, and mutual recognition of vehicles and roadside identity is achieved through the TA in the form of zero-knowledge proof, which achieves privacy protection for the vehicle during authentication. (2) A combined key generation method was devised. The roadside and the TA in this case jointly generate the group key. The TA uses a previously filled quantum key to encrypt the group session key parameter GSPc generated by its quantum random number generator to ensure security, and the roadside obtains the group session key parameter GSPr by calculating the anonymous credentials of all legitimate vehicles to achieve fast updates of the group session key. This scheme achieves forward and backward security while guaranteeing one-at-a-time encryption. The signaling and computation overheads were calculated, and the signaling overhead was reduced by nearly half. In addition, the group key issuance time was significantly reduced compared with other schemes. Through formal security analysis and experimental verification, the security and feasibility of this protocol were proved.
Muhammad Ahmad Nawaz Ul Ghani, Kun She, Muhammad Arslan Rauf, Masoud Alajmi · 6 authors
In recent years, facial recognition technology has become increasingly integrated into society, making privacy protection crucial. Previous techniques offered minimal secrecy safeguards through simple obscuration methods. This paper addresses the strict privacy requirements of face image data by developing a novel framework that synergistically integrates Generative Adversarial Networks (GANs), clustering algorithms, and Blockchain technology. The methodology proposes a cutting-edge Privacy-Preserving Self-Attention GAN (PPSA-GAN) to generate realistic synthetic facial imagery. An integrated mini-batch K-means clustering algorithm anonymizes these images into distinct groupings, maximizing privacy preservation. Blockchain integration complements the system by fortifying trust through decentralized ledgers for transparent yet secure data storage and auditing. Rigorous benchmarking on the CelebA dataset confirms the PPSA-GAN architecture’s state-of-the-art performance, attaining an impressive Inception Score of 13.99 and a Fréchet Inception Distance of 35.50. The mini-batch clustering forms 125 distinct clusters, effectively anonymizing facial attributes within the synthetic images. Blockchain integration further bolsters privacy assurances via tamper-proof historical records, showcasing precision, recall, F1-score, and accuracy values of 0.948, 0.938, 0.943, and 0.947, respectively. This multifunctional framework represents a novel contribution, fostering an ethical technological ecosystem that balances progress and privacy. Prospective deployment horizons encompass identity verification, surveillance infrastructure, and augmentation of medical image repositories, seeding an enlightening future for facial recognition domains.
This article investigates the creation of a decentralized system for biometric-based identity verification, integrating blockchain technology and computer vision. The goal is to provide a secure verification method, minimizing identity fraud and transactional deceptions. The system combines facial recognition, fingerprint scanning, and iris scanning, ensuring precise individual identification. Blockchain guarantees data immutability and decentralization, ensuring biometric data integrity. Computer vision enhances biometric data processing, improving system accuracy and efficiency. Key components include blockchain for secure biometric data storage via Solidity-based smart contracts, computer vision algorithms like convolutional neural networks for facial image analysis, and the OpenPGP library for asymmetric encryption. The system's backend is developed in Python using FastAPI, with a React-based frontend for user interaction. This web application is compatible with modern browsers and integrates seamlessly with existing systems. Results show the development of a novel blockchain-based decentralized identity system, employing a Solidity smart contract for encrypted biometric data storage and a React and FastAPI web application for facial image analysis. The system ensures data privacy through asymmetric encryption and has undergone extensive testing, proving significant advantages over conventional solutions. The discussion emphasizes the system's benefits in secure, efficient, and accurate identity verification, suitable for various sectors like finance, healthcare, and government services. The system's future could involve expanding biometric modalities, algorithmic optimization, and incorporating emerging technologies like the Internet of Things.
Abstract Internet of Medical Things (IoMT) has typical advancements in the healthcare sector with rapid potential proof for decentralised communication systems that have been applied for collecting and monitoring COVID‐19 patient data. Machine Learning algorithms typically use the risk score of each patient based on risk factors, which could help healthcare providers decide about post‐COVID‐19 care and follow‐up where the data privacy is another severe concern. The authors investigate the applicability of a distributed reinforcement learning approach in a Federated Learning (FL) multi‐disciplinary reinforcement system and explores the potential benefits of incorporating Blockchain Technology (BT) in the distributed system. Intermediate dependency features and transactions are avoided by applying Blockchain‐enabled reinforcement FL for the post‐COVID‐19 patient data of IoMT applications. The proposed approach helps to improvise clinical monitoring and ensure secure communication and data privacy in a decentralised manner. The main objective is to improve the efficiency and scalability of the reinforcement FL process in a distributed environment while ensuring data privacy and security through BT for IoMT applications. Results show that proposed approach achieve comparatively high reliability and outperforms the existing approaches.
Abstract Blockchain technology has become an emerging area in recent years due to its capacity to improve the security, dependability, and resilience of distributed systems. Research based on this technique has impacted several firms, including banking, healthcare, data processing, remote sensing, and many others. The key characteristics of blockchain technology that make it appealing are data immutability, transparency, privacy, decentralization, and distributed ledgers. However, there is a chance of a privacy breach with sensitive biometric data. The purpose of this investigation is to examine blockchain‐based biometric applications research. It begins by determining the myriad ways that biometrics and blockchain may work together, including the storage and protection of biometric templates, identity management, and biometric authentication systems. Different biometric applications with respect to blockchain technology are also identified, along with the types of biometric data taken into account, features and capabilities of blockchain technology exploited, and blockchain technology frameworks employed. Finally, the authors seek to investigate blockchain concepts in the biometric domain by evaluating their pros and cons and summarizing the methods developed on blockchain for diverse biometric applications. Additionally, the applications of blockchain‐based biometric systems are highlighted before moving on to open research questions and potential future research areas.
X. Sean Wang, Wei Wang, Cheng Huang, Ping Cao · 6 authors
Identity authentication is an essential element for industrial Internet of Things (IIoT), which guarantees secure access control for various devices. Existing authentication schemes face some security threats, including temporary secret leakage attack, key recovery attack, and forgery attack. In this article, we introduce a blockchain-based certificateless conditional anonymous authentication (BCCA) scheme specifically designed for IIoT. To optimize the authentication efficiency, BCCA employs elliptic curve design to avoid the relatively time-consuming bilinear pairing operation. Additionally, we introduce a precomputation strategy, allowing users to prepare essential materials in advance, and one-time verification support for batch signatures is applied, thus reducing authentication latency. To further enhance the security, random verification checksums are employed to counter key recovery attack, and a combination of long-term and short-term secrets is used to mitigate temporary secret leakage attack. Simulation results demonstrate that our scheme has advantages in both security and computational cost.
Muhammad Ahmad Nawaz Ul Ghani, She Kun, Muhammad Arslan Rauf, Shumaila Khan · 7 authors
In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a blockchain-based decentralized facial recognition system (DFRS) that has been designed to overcome the complexities of technology. The DFRS takes a trailblazing approach, focusing on finding a critical balance between the benefits of facial recognition and the protection of individuals' private rights in an era of increasing monitoring. First, the facial traits are segmented into separate clusters which are maintained by the specialized node that maintains the data privacy and security. After that, the data obfuscation is done by using generative adversarial networks. To ensure the security and authenticity of the data, the facial data is encoded and stored in the blockchain. The proposed system achieves significant results on the CelebA dataset, which shows the effectiveness of the proposed approach. The proposed model has demonstrated enhanced efficacy over existing methods, attaining 99.80% accuracy on the dataset. The study's results emphasize the system's efficacy, especially in biometrics and privacy-focused applications, demonstrating outstanding precision and efficiency during its implementation. This research provides a complete and novel solution for secure facial recognition and data security for privacy protection.
Open access
Biometric Identification and Security
Face recognition and analysis
Advanced Steganography and Watermarking Techniques
In the rapidly evolving environment of wireless medical sensor networks (WMSN) and the internet of medical things (IoMT), remote medical support has seen unprecedented advancements. It is essential that the data relayed from the sensors must be trustworthy and unaltered, and that the sensors themselves are genuine. Wireless networks, however, have inherent vulnerabilities. In addition, since WMSN is directly linked to patients’ lives, its continuous availability is crucial. Considerable efforts have been made to maintain the integrity and authenticity of such data. However, many studies have failed to address the problem of a single point of failure (SPOF). This issue has been particularly detrimental to patients who require ongoing management. To address this issue and ensure the protection of the authenticity and integrity of patient data, we suggest the implementation of an authentication scheme based on blockchain technology. In 2022, Yu et al. introduced a blockchain-integrated authentication and key generation scheme for WMSN using Physical Unclonable Functions (PUFs), effectively addressing the SPOF problem by conducting mutual authentication through smart contracts without relying on centralized servers. Our research found that this scheme inadvertently shared critical parameters, including challenge-response pairs and important private keys, with the blockchain network, making it vulnerable to various breaches. We present an enhanced protocol designed to mitigate these security challenges. By limiting the data interaction with smart contracts and ensuring only relevant parties access crucial parameters, our approach reduces the risk of public information disclosure on the blockchain. This not only mitigates the SPOF issue but also efficiently helps in prevention of physical attacks. We prove that our proposed system prevents known security vulnerabilities through informal and formal analysis using the Scyther, Proverif, and BAN logic. Furthermore, the proposed scheme offers 67.37% reduction in computation costs and 3.67% in communication costs, presenting an efficient and secure solution for WMSN in the IoMT landscape.
In recent times, the proliferation of fake certificates has become a growing concern, undermining the credibility of educational and professional qualifications. This paper presents a system based on blockchain designed to avoid the issuance and circulation of counterfeit certificates using Non-Fungible Tokens (NFTs). Using the unchangeable and see-through nature of blockchain technology, the proposed system ensures secure verification and authentication of certificates. By employing NFTs, each certificate is tokenized, making it unique, indivisible, and tamper-resistant. The system provides a robust and trustworthy platform for institutions, employers, and individuals to eradicate the menace of fake certificates, promoting trust and integrity in the certification ecosystem.
User Authentication and Security Systems
Biometric Identification and Security
Advanced Steganography and Watermarking Techniques
Abstract In recent years, face biometrics recognition systems are a wide space of a computer usage which is mostly employed for security purpose. The main purpose of the face biometrics recognition system is to authenticate a user from a given database. Due to the widespread expansion of the surveillance cameras and facial recognition technology, a robust face recognition system required. The recognition system needs to store a large number of training samples in any storage unit, that time hackers can access and control that data. So, Protecting and managing sensitive data is essential object. This requires a technique that preserve the privacy of individuals, maintain data integrity, and prevent information leakage. The storage of biometric templates on centralized servers has been associated with potential privacy risks. To address this issue, we have developed and implemented a proof-of-concept facial biometric identification system that uses a private Blockchain platform and smart contract technology. So, the proposed approach is presented a secure and tamper-proof from data breaches as well as hacks with data availability, by using the Blockchain platform to store face images. This paper aims to utilize Blockchain technology to identify individuals based on their biometric traits, specifically facial recognition system makes it tamper-proof (immutable) ensuring security. The system consists of enrolment and authentication phases. Blockchain technology uses peer-to-peer communication, cryptography, consensus processes, and smart contracts to ensure the security. The proposed approach was tested on two popular datasets: CelebFaces Attributes (CelebA) and large-scale face UTKFace datasets. The experimental results indicate that the system yields highly performance outcomes, as evidenced by the Equal Error Rate (EER) values of 0.05% and 0.07% obtained for the CelebA and UTKFace datasets, respectively. The system was compared to three baseline methods and scored the lowest Equal Error Rate.
In today's world, secure and efficient biometric authentication is of keen importance. Traditional authentication methods are no longer considered reliable due to their susceptibility to cyber-attacks. Biometric authentication, particularly fingerprint authentication, has emerged as a promising alternative, but it raises concerns about the storage and use of biometric data, as well as centralized storage, which could make it vulnerable to cyber-attacks. In this paper, a novel blockchain-based fingerprint authentication system is proposed that integrates zk-SNARKs, which are zero-knowledge proofs that enable secure and efficient authentication without revealing sensitive biometric information. A KNN-based approach on the FVC2002, FVC2004 and FVC2006 datasets is used to generate a cancelable template for secure, faster, and robust biometric registration and authentication which is stored using the Interplanetary File System. The proposed approach provides an average accuracy of 99.01%, 98.97% and 98.52% over the FVC2002, FVC2004 and FVC2006 datasets respectively for fingerprint authentication. Incorporation of zk-SNARK facilitates smaller proof size. Overall, the proposed method has the potential to provide a secure and efficient solution for blockchain-based identity management.