Blockchain Papers

Dharmesh Kumar Sonkar, Sarvpal Singh, Amrendra Singh Yadav

No abstract is available for this record.

Daeun Hyeon, Sungchae Park, Heung Youl Youm, Jae Yeol Jeong

No abstract is available for this record.

Dharmesh Kumar Sonkar, Sarvpal Singh, Amrendra Singh Yadav

No abstract is available for this record.

Veerpal Kaur, Devershi Pallavi Bhatt, Sumegh Tharewal, Pradeep Kumar Tiwari

To managing identities in a secure and decentralized manner, new opportunities have emerged because of recent breakthroughs in blockchain technology and biometric authentication. Blockchain is different from traditional biometric systems in that it is an unchangeable, distributed ledger that runs safe, decentralized code. Traditional biometric systems store data in one location and can’t be updated. Traditional biometric systems have some flaws, including template tampering, channel interception, and comparator overrides. So, the proposed work presents a Distributed Multimodal Biometric Security System with Blockchain to handle such issues. This system uses 3D face and 3D ear biometrics with blockchain technology, which comprises IPFS, smart contracts, and decentralized applications. Features from 3D face and 3D ear are embedded into a single multimodal template, which then undergoes encryption and storage on IPFS via content-addressed storage. The Content Identifier (CID) and data are then archived by smart contracts on the blockchain to maintain data integrity, security, verifiability, and immutability. In this way, a person can prove his identity without using any central services, further improving privacy. Blockchain consensus and the smart-contract-based access control mechanism further provide security, audibility, and simplicity to P2P transactions in biometric enrolment testing results show that feature extraction takes from 120 ms to 300 ms, uploading to IPFS takes between 200 and 600 ms, and completing blockchain transactions on local private network takes from 0.5 to 1 s, using 117,519 gas per enrolment. Additional analysis on the Ethereum Sepolia test network reveals that transaction fees change depending on network conditions, but gas consumption stays deterministic. The suggested solution is resistant to typical attacks like replay, interception, and template alteration; it is also irreversible, revocable, and unlinkable, according to security analysis conducted under a formal adversarial model.

R. Sridevi, Satu Sai Sanjana Yadav

Facial recognition has become an essential technology in modern surveillance and law enforcement for the automatic identification of individuals from images and video streams. Conventional facial recognition techniques often experience reduced accuracy due to variations in illumination, facial pose, occlusion, low-quality images, and aging effects. To address these challenges, this paper proposes a Blockchain-Based Criminal Recognition and Evidence Management System that integrates advanced deep learning models with secure blockchain technology. The proposed system employs Multi-task Cascaded Convolutional Networks (MTCNN) for accurate face detection and facial alignment, followed by StyleGAN for age progression and age transformation to generate age-invariant facial representations while preserving the individual's identity. The transformed facial images are then processed by a Convolutional Neural Network (CNN)-based facial recognition model to extract discriminative facial features and accurately identify suspects by comparing them with a criminal database. Upon successful recognition, the system automatically generates real-time alerts for authorized personnel and securely stores recognition results, timestamps, confidence scores, and evidence metadata on a blockchain using Web3.py and Ganache, ensuring data integrity, transparency, traceability, and protection against unauthorized modification. By combining robust face detection, ageinvariant facial recognition, and tamper-proof evidence management, the proposed system provides an accurate, secure, and reliable solution for modern criminal identification and digital forensic investigations.

G. Padma, Venkata Akhilesh Ranga Reddy, Sumithra Devi. K A, B. Buvaneswari · 5 authors

No abstract is available for this record.

Raniyah Wazirali, Fatma Foad Ashrif, Rami Ahmad

The rapid adoption of smart-home and Internet-of-Things (IoT) devices has intensified the need for privacy-preserving biometric authentication that is both secure and computationally efficient. This paper presents Hybrid-HE LLE, a practical framework that combines Locally Linear Embedding (LLE) with selective homomorphic encryption to protect face-recognition features in resource-constrained IoT environments. Unlike cloud-centric outsourcing, the proposed system performs all heavy linear-algebra operations within a semi-trusted Insider Hub, ensuring data sovereignty, low latency, and verifiable computation without revealing raw facial features. A sparse orthogonal or Toeplitz transform first obfuscates feature vectors, after which sensitive coefficients are selectively encrypted using CKKS-based polynomial encoding. Homomorphic hashing and optional zero-knowledge proofs guarantee the integrity and auditability of outsourced results. Experiments on the ORL and LFW datasets demonstrate over 94 % Rank-1 accuracy, while reducing client computation by 92 %, uplink bandwidth by 80 %, and energy usage by 55 %, with authentication latency below 120 ms on a Raspberry Pi 4-class edge device. The framework provides formal protection against IND-CPA, EUF-CMA, and IND-CCA adversaries and maintains compliance with GDPR/HIPAA requirements. Hybrid-HE LLE thus offers a scalable, secure, and real-time solution for privacy-preserving biometric access in modern IoT communication systems.

Vanshika Joshi Sneha DL and Shruti M Jolad

ABSTRACT In the contemporary digital landscape, high-profile individuals including celebrities, executives, political leaders, and public officials face unprecedented threats from online impersonation, sophisticated misinformation campaigns, AI-generated deepfakes, and fraudulent social media profiles. The convergence of generative artificial intelligence technologies and social media platforms has dramatically expanded the attack surface, enabling malicious actors to create synthetic identities, manipulate multimedia content, and spread false narratives with alarming ease and speed. Existing security solutions remain fragmented, requiring extensive manual intervention and lacking the capability for real-time monitoring and automated threat response, thereby leaving critical gaps in digital protection for vulnerable public figures. This research paper presents GuardIQ, an integrated, fully automated, end-to-end VIP Threat Detection and Monitoring Platform that combines post-quantum cryptography, multi-factor biometric authentication, artificial intelligence-powered threat detection, and blockchain-based evidence preservation. The platform architecture is built upon seven core pillars: quantum-secure biometric registration utilizing Kyber Key Encapsulation Mechanism (KEM), real-time threat detection engine monitoring multiple social media platforms, AI-powered content verification distinguishing authentic media from AI-generated deepfakes, automated fake profile detection comparing discovered accounts against registered handles, live analyzer for instant authenticity verification, immutable evidence collection using Web3 technologies, and unified dashboard providing comprehensive threat intelligence visualization. GuardIQ employs CRYSTALS-Kyber post-quantum cryptographic algorithms (Kyber512 for lightweight mobile endpoints and Kyber768/1024 for enterprise deployments) combined with AES-256-GCM symmetric encryption to ensure quantum-resistant data protection. The biometric registration module captures facial recognition data, voice patterns, gesture signatures, and official social media handles, all protected through quantum-safe encryption. Large Language Models (LLMs) integrated within the threat detection engine perform real-time classification of suspicious content, achieving 92-97% accuracy in identifying impersonation attempts, misinformation campaigns, and image misuse across platforms including Twitter, Facebook, Instagram, and LinkedIn. The AI content detection module leverages advanced deep learning architectures including Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) for sequential pattern detection, and transformer-based models for multimedia authenticity verification. Experimental results demonstrate the system's capability to distinguish AI-generated content from authentic material with confidence scores exceeding 94%, providing early detection of deepfakes and synthetic media targeting VIP credibility. The fake profile detection algorithm analyzes multiple parameters including account creation timestamps, username patterns, biographical information, follower-to-following ratios, engagement metrics, and posting behavior patterns to identify fraudulent accounts with 89% precision. Evidence collection is facilitated through Web3-based blockchain infrastructure ensuring tamper-proof, immutable storage of all flagged incidents, suspicious posts, and detected impersonations. This cryptographically verifiable evidence chain supports legal proceedings and investigative actions by providing irrefutable proof of malicious activities. The unified dashboard aggregates threat intelligence from all modules, presenting real-time alerts, authenticity scores, risk assessments, and recommended remediation actions through intuitive visualizations requiring minimal manual oversight. Performance evaluation reveals that post-quantum TLS handshakes introduce only 5-10 milliseconds additional latency compared to classical TLS implementations, demonstrating practical feasibility for production deployment. The automated threat detection pipeline reduces incident response time by 72% compared to manual monitoring approaches, while the quantum-resistant encryption framework ensures long-term security against emerging quantum computing threats. System architecture supports horizontal scalability through microservices deployment, containerization using Docker and Kubernetes orchestration, and cloud-native infrastructure compatible with AWS, Azure, and Google Cloud Platform. This research addresses the urgent need for comprehensive digital protection solutions in an era where AI-generated content, quantum computing capabilities, and sophisticated social engineering attacks converge to create unprecedented risks for public figures. GuardIQ represents a paradigm shift from reactive security measures to proactive, automated threat intelligence platforms capable of defending high-profile individuals against modern digital adversaries while maintaining usability, scalability, and legal compliance. Keywords : VIP Protection, Post-Quantum Cryptography, Kyber KEM, Deepfake Detection, AI Content Verification, Biometric Authentication, Threat Intelligence, Social Media Monitoring, Blockchain Evidence, Web3 Security, Impersonation Detection, Misinformation Prevention, Large Language Models, Zero- Trust Architecture, Quantum-Safe Encryption, Identity Verification, Automated Security Response, Digital Reputation Management

Anushka Tyagi, Gauransh Verma, Aman Sharma, Aanya Goel · 5 authors

Facial recognition technology has been recognized as a powerful tool for enhancing security and authentication, especially in transaction systems. However, serious risks—like data breaches and unauthorized access—are presented by traditional methods that store biometric data in centralized databases. To address these concerns, a decentralized facial biometric system built on blockchain and smart contracts has been developed. User privacy and security are prioritized by this approach. Instead of being reliant on a central database, transactions are securely verified by our system through a comparison of users’ facial biometrics with data that has already been stored in a distributed, tamper-resistant way. By distributing the data across a decentralized network, the system minimizes the risk of unauthorized access or data breaches. This setup not only strengthens security but also builds trust with users by protecting their privacy—while still delivering consistent and reliable performance.

Wen‐Bin Hsieh

Biometric authentication is adopted in many access control scenarios in recent years. It is very convenient and secure since it compares the user’s own biometrics with those stored in the database to confirm their identification. Since then, with the vigorous development of machine learning, the performance and accuracy of biometric authentication have been greatly improved. Face recognition technology combined with convolutional neural network (CNN) is extremely efficient and has become the mainstream of access control systems (ACSs). However, identity information and access logs stored in traditional databases can be tampered by malicious insiders. Therefore, we propose a face recognition ACS that is resistant to data forgery. In this paper, a deep convolutional network is utilized to learn Euclidean embedding (based on FaceNet) of each image and achieve face recognition and verification. Quorum, which is built on the Ethereum blockchain, is used to store facial feature vectors and login information. Smart contracts are made to automatically put data into blocks on the chain. One is used to store feature vectors, and the other to record the arrival and departure times of employees. By combining these cutting‐edge technologies, an intelligent and immutable ACS that can withstand distributed denial‐of‐service (DDoS) and other internal and external attacks is created. Finally, an experiment is conducted to assess the effectiveness of the proposed system to demonstrate its practicality.

R P Tejushree, A.V.S.S. Prasad, C R Vedashree, Vishaka Rani Chandramule · 6 authors

Technology advancements have prompted researchers to investigate electronic voting, or "e-voting," to update and simplify the electoral process. In this research, we present a novel solution to these problems by fusing deep learning methods specifically, facial detection with blockchain technology to create an electronic voting system. To further enhance the security and integrity of the voting process, we apply deep learning techniques for facial detection. The voter's identification is then confirmed by deep learning models analyzing this data, which stops fraudulent voting and guarantees that only qualified people take part in the election process. Furthermore, our approach places a high priority on protecting voter privacy by utilizing methods like homomorphic encryption and zero-knowledge proofs, which enable vote counting without jeopardizing the voter's identity. Because blockchain technology is transparent, audits and verification are made easier, allowing interested parties to confirm the fairness of the electoral process. Our suggested electronic voting system provides a strong solution that tackles the primary issues of security, integrity, and privacy by merging deep learning combined with blockchain technologies for facial recognition.

D. Gayathri, V. Raghavendran

With more and more data being generated, identifying a secure and effective data access framework has become a crucial research issue. Technological evolutions have been made in numerous areas to name a few being, agriculture, industry and specifically healthcare systems. Healthcare has experienced several remarkable alternates as the comprehensive transformation from document-based storage to electronic health records (EHR). The centralized mechanisms utilized by medical institutions for the Electronic Medical Records (EMR) management and transfer can be highly susceptible to security and privacy menaces. Blockchain have been a fascinating research area over a long period of long time and the advantages it imparts have been utilized by a number of several industries. In a similar manner, the healthcare sector stands to ease extensively from the blockchain technology owing to security and privacy. In this work to securitize patient record and access using a method called Ethereum Smart Contract Graph Embedded and Pyramid Network (ESCGE-PN) is proposed. In this paper, we first design a Graph Embedding-based Neural Network and incorporate it into Differentiable Permutation Invariant Ethereum Smart Contract to securitize patient medical record. Our proposed Differentiable Permutation Invariant Ethereum Smart Contract Graph Embedding-based Neural Network makes managing healthcare records using Permutation Invariant operator to accept arbitrary samples and maps patient records with the corresponding face images to form blocks in blockchain. We then propose novel Feature Fusion Pyramid Network Cosine Similarity-based face recognition for patient information access, which uses blockchain along with the the Feature Fusion Pyramid Network to address security concerns and enables selective sharing of medical records between doctors and patients. To measure the ESCGE-PN methods performance, four different performance metrics, data confidentiality, data integrity, recognition accuracy and recognition error are validated and analyzed. As a result, ESCGE-PN method achieved a higher performance compared to other state-of-the-art methods.

Yanming Zhu, Xuefei Yin, Alan Wee‐Chung Liew, Hui Tian

With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a comprehensive overview of privacy-preserving techniques in medical image analysis, including encryption, differential privacy, homomorphic encryption, federated learning, and generative adversarial networks. We explore the application of these techniques across various medical image analysis tasks, such as diagnosis, pathology, and telemedicine. Notably, we organizes the review based on specific challenges and their corresponding solutions in different medical image analysis applications, so that technical applications are directly aligned with practical issues, addressing gaps in the current research landscape. Additionally, we discuss emerging trends, such as zero-knowledge proofs and secure multi-party computation, offering insights for future research. This review serves as a valuable resource for researchers and practitioners and can help advance privacy-preserving in medical image analysis.

Yuan Cao, Fanlei Meng, Xinzheng Shang, Jie Gui · 5 authors

With the growing popularity of cloud services, companies and individuals outsource images to cloud servers to reduce storage and computing burdens. The images are encrypted before outsourcing for privacy protection. It has become urgent to solve the privacy-preserving image retrieval problem on the cloud. There are three main challenges in this area. First, how can we achieve high retrieval accuracy on the encryption domain? Second, how can we improve efficiency in large-scale encrypted image retrieval? Third, how can we ensure the reliability of the retrieval results? The existing schemes only consider some of these characteristics and the retrieval accuracy is insufficient. In this paper, we propose a privacy-preserving large-scale image retrieval framework with vision graph convolutional neural network hashing (ViGH). To the best of our knowledge, this is the first framework that is able to address all the above challenges with more advanced accuracy performance. To be specific, cycle-consistent adversarial networks and vision graph convolutional networks (ViG) are utilized to increase retrieval accuracy. By embedding encrypted images into hash codes, we can obtain high retrieval efficiency by Hamming distances. Cloud servers store the hash codes on the blockchain (Ethereum). The retrieval algorithm on the smart contracts and the consensus mechanism of blockchain ensure reliability of the retrieval results. The experimental results on three common datasets verify the effectiveness and efficiency of the proposed privacy-preserving image retrieval framework. The reliability of the retrieval results is ensured by the consensus mechanism of blockchain with no need for verification.

Chaitali Patil, Sanjita Jain, Rupprashik A. Khare, Samyak Lahire

This research paper explores the zero-knowledge proofs (ZKPs) and integration of blockchain technology to develop a secure age verification system, specifically aimed at verifying the age of a person who is driving a vehicle (driver) as at least 18 years in the context of traffic management. Traditional authentication methods like passwords and biometrics have significant vulnerabilities, such as being susceptible to brute-force attacks, phishing, and data breaches. Unlike traditional systems and self-sovereign identity (SSI) solutions, ZKPs ensure the most secure method for authentication and verification without revealing sensitive information. The proposed implemented system utilizes blockchain-based verifiable credentials, decentralized identity, ZKPs and Polygon ID Wallet for verifying driver credentials securely. By leveraging decentralized ledgers, cryptographic protocols and zero-knowledge our system maintains transparency and immutability of records while safeguarding individual privacy and security. This paper also focuses future directions of the proposed system underscoring its potential to transform user verification processes across different sectors with high privacy requirements.

Yuhan Chen, Mingmei Lyu, Ho Yin Kan, Mei Pou Chan · 6 authors

Aviation information systems are a key component in ensuring efficient and smooth air transport operations. In this regard, the transfer of passenger information between parties is of paramount importance. With the continuous improvement of biometrics technology, this kind of individual identification that can provide accurate and unforgeable identification is widely used in various fields. This research presents the significance and effective application scenarios of facial recognition in biometrics in air transport operations. Due to the characteristics of aviation information systems, Distributed Ledger Technology (DLT) is used in this study for secure and private transmission of facial recognition information. Distributed systems can give a transparent and secure platform to multiple parties to access sensitive passenger data. This study uses the Corda framework as the DLT that supports CorDapp development. Based on the above techniques, this study proposes two feasible application scenarios. One is a baggage match detection system to prevent misplaced baggage, and the other is an iAPIS system that transmits passenger information in real-time communication between airlines and border control agencies. This article details how to apply the research in these two scenarios, as well as the benefits and implications of the applications. Finally, this article presents an outlook for future development and feasible directions for improvement.

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.

G. Uteyev, R. F. Gibadullin

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.

Shreyansh Sharma, Anil Kumar Saini, Santanu Chaudhury

No abstract is available for this record.

H.C. Lee, Yi‐Ting Chen

Abstract This paper presents a novel method for decentralized storage in deep-learning-based face recognition systems using the Hierarchical Navigable Small World (HNSW) algorithm. The proposed solution utilizes Ethereum smart contracts, which acts as highly available data storage systems for storing identifiable data for authorized personnel. In addition, the solution is integrated with a centralized vector database that is in charge of vector indexing, searching and associating face embeddings to an identity on the Ethereum blockchain with anonymous hashes. Vector indexing and search processes involve different machine learning algorithms that enable computations to be carried out in a reasonable time with good matching accuracy. Specifically, we compared different approaches and selected the HNSW algorithm. Accordingly, we successfully implemented a prototype of a reliable and privacy-focused decentralized face identification system for areas under government surveillance, such as customs inspection sites. In our measurements, the system could handle 20,000 face vectors easily with high matching accuracy, and the performance could be further improved using more powerful hardware. Finally, we also propose additional methods to further scale up the system to handle millions of face vectors.

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.

Jungwon Seo, Hankyeong Ko, Sooyong Park

As the metaverse gains traction, the importance of metaverse security research becomes increasingly evident. While there has been research on authenticating users in the metaverse, there is a notable gap in research concerning the authentication of specific spaces within the metaverse. This paper addresses this gap by proposing a novel user-centric blockchain-based authentication approach that incorporates space authentication. The proposed approach leverages blockchain smart contracts to authenticate users using cosine similarity metrics. A significant advantage of this approach its ability to establish user-centric authentication by seamlessly integrating metaverse and blockchain technologies, all without the need for a centralized authority. In this paper, we not only evaluate the security of our proposed approach but also conduct experiments to determine the cosine similarity threshold and assess its feasibility within a metaverse environment.

Sherif Hamdy Gomaa Salem, Ashraf Yehia Hassan, Marwa S. Moustafa, Mohamed Nabil Hassan

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.

Pranay Kothari, Deepak Chopra, Manjot Singh, Shivam Bhardwaj · 5 authors

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.