Blockchain Papers

Dharmesh Kumar Sonkar, Sarvpal Singh, Amrendra Singh Yadav

No abstract is available for this record.

Henry Ohiani Ohize, Adeiza James Onumanyi, Lukman Adewale Ajao, Buhari Ugbede Umar · 9 authors

Despite significant advances in electronic voting technologies, voter accreditation in many electoral systems remains vulnerable to identity fraud, database tampering, equipment failure, and centralized security breaches. Existing accreditation solutions often rely on single-modal biometric authentication and centralized architectures, limiting their robustness, transparency, and public trust. This paper proposes a Blockchain-based Bimodal Voter Accreditation System (Block-BVAS), together with a practical framework for its deployment in electronic voting systems. The proposed system integrates multimodal biometric authentication using facial and fingerprint recognition with a private Ethereum blockchain and conventional cryptographic mechanisms to provide secure, tamper-resistant, and auditable voter accreditation to provide secure, decentralized, and tamper-resistant voter accreditation. A Raspberry Pi 5 serves as the embedded processing platform, demonstrating the feasibility of implementing the framework on cost-effective hardware. By combining distributed-ledger technology with encrypted biometric verification, the proposed architecture enhances the integrity, confidentiality, and immutability of election-related records while addressing limitations associated with single-factor authentication and conventional centralized record management. Experimental evaluation of the biometric authentication module performed effectively, with fingerprint recognition achieving an average authentication accuracy (AA) of 97.8% and facial recognition averaging 95.1%. The blockchain storage overhead (BSO) displayed a near-linear growth pattern relative to the number of transactions, consistent with theoretical expectations for blockchain architectures. Reliability analysis indicated system uptime exceeding 95%, with only minimal operational failures recorded during the test period. This blockchain implementation further demonstrated reliable transaction processing and secure record management, indicating the effectiveness of the proposed Block-BVAS in enhancing the security, transparency, and trustworthiness of electronic voter accreditation.

Abhay Kumar Yadav, Virendra P. Vishwakarma

No abstract is available for this record.

Saad Altamimi, Saad Alahmari, Ibrahim Alghamdi, Yousef Alhaizaey · 5 authors

Today, biometric authentication has become a central component of user security in social governance systems, where each government department demands access to user-specific data that varies across agencies. However, storing such data in centralized repositories increases serious privacy concerns, as unrestricted access by multiple entities maximizes the risk of data leakage. To address this, our research presents a novel biometric authentication system integrating robust privacy-preserving techniques, built on advanced deep learning architectures and differential privacy algorithms. A blockchain ledger integrated with a Merkle tree is used to securely store user identities, providing tamper-evident cryptographic validation of registered users. We further develop a novel hybrid model by integrating a pre-trained Vision Transformer (ViT) with a differential privacy-based machine learning enhanced training strategy, wherein the model is trained on noise-induced images to resist inference attacks. The system without differential privacy achieves 90.80% accuracy, 0.94 precision, 0.91 recall, and an F1-score of 0.90 in the standard configuration, while the differentially private model maintains 68.97% accuracy with ε = 6.2, ensuring a strong privacy—accuracy balance. The evaluation confirms that our proposed model, incorporating differential privacy, provides a secure and scalable solution for managing sensitive citizen data while achieving reliable performance in privacy-aware biometric verification for real-world e-governance applications.

Anithalakshmi V¹, Raja P², N Sripriya, M Lavanya

No abstract is available for this record.

Dharmesh Kumar Sonkar, Sarvpal Singh, Amrendra Singh Yadav

No abstract is available for this record.

Hui Cui, Haoze Cheng, James Boorman

Biometric authentication offers enhanced usability for digital payments, but traditional centralized architectures suffer from single points of failure. While blockchain integration promises decentralized trust, existing solutions often store biometric helper data (e.g., fuzzy commitments) directly on-chain. We demonstrate that such transparency, even in permissioned settings, exposes low-entropy biometric inputs to offline brute-force attacks and identity-linkage risks if the immutable ledger is accessed by malicious nodes. To resolve this transparency–privacy paradox, we propose a threshold-based decentralized authentication framework. Unlike prior works, our protocol keeps all sensitive Biometric-Enhanced Key Derivation (BEKD) tokens entirely off-chain, using the blockchain solely for freshness enforcement. We provide a game-based security analysis of brute-force resistance, unforgeability, and unlinkability. Our experimental results demonstrate that our scheme’s gas cost is acceptable, offering a robust solution for self-sovereign biometric identity.

Ihsan H. Hussein

Newborn misidentification poses serious patient safety and accountability problems, but errors can be traced through the use of a blockchain to create an audit trail. However, a blockchain storing raw or even hashed biometric templates for individual identities is not acceptable for privacy reasons. This work redefines our prior work (1) to form a privacy-preserving audit protocol that isolates the processes of capturing a biometric and matching it against a database of known identities to an external Service Provider and the processing of the blockchain to a permissioned Ledger that contains only pseudonymous audit commitments related to keyed entries on the Ledger. This work describes an implementation of this protocol in Solidity 0.8.30 and provides metrics for the gas use and latency of the smart contract for 100 iterations of 100 total Enrollment and Verification Workflows each. Twenty Adversarial Functional Tests are also described that attempt to place the system into an invalid state, as well as four additional tests that assess the effect of batched submission to the smart contract of multiple keyed audit commitments. The smart contract processing throughput is also determined for a batch of submissions, finding a maximum local throughput of 60.2 tx/s. A further 50,000 randomized reference-model transitions of the system’s internal reference-model were then made (involving a total of 57,345,087 invariant checks, all of which passed), as well as a measurement of the time taken to generate an HMAC-SHA-256-sized commitment for 10,000 iterations (local median time = 0.002 ms). The results of this work provide a solid foundation for the blockchain component of BIBIS, but it is not intended to provide any insights into the accuracy of neonatal biometric matching, the presentation attack resistance of the system, or even the usability of BIBIS by clinical end-users. The results also do not comment on the finality of QBFT-based commits to a blockchain.

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.

Madi Gali, Aray Kassenkhan, Y. Chinibayev, A. M. Abshukirova · 5 authors

Static, one-time authentication mechanisms such as passwords and PINs are increasingly inadequate for protecting mobile devices throughout an active session. Behavioral biometric continuous authentication (BBCA) addresses this gap by passively monitoring user-specific interaction patterns—keystroke dynamics, touch and swipe gestures, gait, and motion—to verify identity on an ongoing basis. This systematic review synthesizes 80 studies selected via a PRISMA-compliant protocol from IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, Web of Science, and SpringerLink (2017–2025). We examine behavioral and multimodal biometric modalities, machine learning approaches ranging from classical classifiers to deep sequence and transformer architectures, and their integration with intelligent personal agents, wearable devices, and IoT/edge infrastructures. Security analyses cover spoofing, adversarial and generative attacks, mimicry, and model-level threats including membership inference and reconstruction. Privacy-preserving mechanisms—cancelable biometrics, Bloom filter encodings, zero-knowledge proof protocols, federated learning, and blockchain-based identity management—are evaluated against practical trade-offs in energy consumption and latency on resource-constrained devices. Key research gaps are identified: the absence of standardized adversarial benchmarks, lack of end-to-end pipeline evaluations under simultaneous adversarial and privacy threat models, and limited user-centered studies on consent and acceptance of privacy-preserving mechanisms under frameworks such as GDPR. Recommended future directions combine adaptive multimodal fusion, privacy-preserving cryptography, energy-aware modality selection, and interdisciplinary human-centered evaluation to advance practical, resilient continuous authentication for mobile and assistant-enriched environments.

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.

I. Smid -Woelders

PrismEco is the showcase demonstration of the Prism Ecosystem. Where the other component demos each illustrate one capability in isolation, PrismEco shows the complete authentication triangle in a single flow: biometric authentication via WebAuthn, a Zero-Knowledge Proof generated in the browser, and NFC presence verification via a physical tag. This technical note follows a single user through the complete login flow on prismeco.globalsecurity.nu. At each step, it documents what the server receives and what it does not receive. The goal is to make visible what is structurally invisible by design: that a working authentication system can process a login without ever knowing who the user is. The three factors are verified independently and must all succeed for the session to open. No single factor is sufficient on its own. The combination is structurally resistant to remote attacks: an attacker would need to compromise biometrics, the device, and physical proximity simultaneously. The complete authentication triangle has been proven in a working PoC as of 12 June 2026. WebAuthn registration and login, ZKP generation and server-side verification (proven 10 June 2026), and NFC tap confirmation with RELAY_TOKEN verification (proven 12 June 2026) all function as an integrated flow on live infrastructure at prismeco.globalsecurity.nu. Screenshots in this document are taken from the live running demonstration. All claims are classified by status: proven in PoC, follows from open standard, or architectural design choice. Part of the Prism Ecosystem. Full technical architecture: The Prism Protocol, Invention Disclosure v20, DOI: 10.5281/zenodo.20029291.

Arvind Vijayakumar

ChitraVault is an exploratory conceptual authentication architecture that investigates whether geometric visual traversal patterns, drawn from the Chitrakavi (சித்திரக்கவி) classical Tamil literary tradition, can augment password vault security by adding a spatial-behavioral dimension to cryptographic key derivation. This paper proposes the Visual Pattern Key Derivation Function (VP-KDF), which combines a user-drawn Chitrakavi geometric pattern, a text passphrase, and a hardware-bound device fingerprint as inputs to Argon2id key stretching. The framework maps four classical Chitrakavi patterns — Chakra Bandha (wheel), Naga Bandha (serpent), Gomutrika (zigzag), and Thiruezhukkootrirukkai (triangle) — onto distinct cryptographic roles within a zero-knowledge password vault architecture. This work is framed as an exploratory research program, not a finished cryptographic system. All security arguments are bounded by stated assumptions and require empirical and cryptanalytic validation. Future work includes controlled user studies, formal security proofs, and prototype evaluation. Author: Arvind VijayakumarIndependent ResearcherMay 2026

I.H.H.N.T.V. Prasad N.A., Mulagala Sandhya

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Charles Hooper

The proliferation of sophisticated AI and bot networks necessitates robust methods for verifying human uniqueness and liveness in digital ecosystems. Existing Proof-of-Personhood (PoP) solutions rely on centralized authorities, invasive static biometrics, or socially-correlatable data, creating vulnerabilities in privacy, security, and accessibility. We introduce the Entros Protocol, a decentralized framework for PoP and Self-Sovereign Identity built on Solana. The core innovation is temporal consistency: the assertion that human identity is best proven not by a static secret, but by the bounded, chaotic drift of biological and behavioral patterns over time. The framework captures multi-modal behavioral data (voice prosody, hand tremor, touch dynamics) during a configurable behavioral challenge, extracts a 308-dimensional feature vector, and produces a 256-bit locality-sensitive hash via SimHash. A Groth16 zero-knowledge proof verifies that consecutive fingerprints fall within a bounded Hamming distance without revealing either value. Attestations are anchored to non-transferable identity tokens (SPL Token-2022) with progressive Trust Scores. We provide formal security definitions, analyze the protocol against replay, synthesis, and Sybil attacks, introduce a graduated trust model distinguishing first-time liveness checks from sustained temporal consistency, and present benchmarks from a working implementation deployed on Solana devnet.

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

No abstract is available for this record.

Yoshiyuki Ootani

IoT location services accept client-reported GPS coordinates at face value, yet spoofing is trivial with consumer-grade tools. Existing spoofing detectors output a binary decision, forcing system designers to choose between high false-deny and high false-accept rates. We propose a graduated trust gate that computes a multi-signal integrity score and maps it to three actions: PROCEED, STEP-UP, or DENY, where STEP-UP invokes a stronger verifier such as a zero-knowledge proximity proof. A session-latch mechanism ensures that a single suspicious fix blocks the entire session, preventing post-transition score recovery. Under an idealized step-up oracle on 10,000 synthetic traces, the gate enables strict thresholds (theta_p = 0.9) that a binary gate cannot safely use: at matched false-accept rate (11%), the graduated gate maintains zero false-deny rate versus 0.05% for binary, with 5 microseconds scoring overhead. Real-device traces from an Android smartphone demonstrate the session-latch mechanism and show that a nearby mock location (~550 m) evades theta_p = 0.7 but is routed to step-up at theta_p = 0.9. Signal ablation identifies a minimal two-signal configuration (F1 = 0.84) suitable for resource-constrained scoring layers.

Charles Hooper

The proliferation of sophisticated AI and bot networks necessitates robust methods for verifying human uniqueness and liveness in digital ecosystems. Existing Proof-of-Humanity (PoH) solutions rely on centralized authorities, invasive static biometrics, or socially-correlatable data, creating vulnerabilities in privacy, security, and accessibility. We introduce the IAM Protocol, a decentralized framework for PoH and Self-Sovereign Identity built on Solana. The core innovation is temporal consistency: the assertion that human identity is best proven not by a static secret, but by the bounded, chaotic drift of biological and behavioral patterns over time. The framework captures multi-modal behavioral data (voice prosody, hand tremor, touch dynamics) during a configurable behavioral challenge, extracts a 134-dimensional feature vector, and produces a 256-bit locality-sensitive hash via SimHash. A Groth16 zero-knowledge proof verifies that consecutive fingerprints fall within a bounded Hamming distance without revealing either value. Attestations are anchored to non-transferable identity tokens (SPL Token-2022) with progressive Trust Scores. We provide formal security definitions, analyze the protocol against replay, synthesis, and Sybil attacks, introduce a graduated trust model distinguishing first-time liveness checks from sustained temporal consistency, and present benchmarks from a working implementation deployed on Solana devnet.

Punya Shree J, Surabhi Saxena, Neha Singhal

Information security is built on authentication, and foundational passwords and PINs are no longer sufficient to change cyber threats. The given paper uses the model by Bonneau et al. (that is, The Quest to Replace Passwords) to qualitatively compare the traditional knowledge factors with the newly emerged solutions such as biometrics, behavioral analysis, FIDO2/passkeys, multi-factor schemes, and Zero-Knowledge Proofs according to their security, usability, deployability, and privacy. Our analysis summarizes the strengths, weaknesses and threat models of each of the categories and then summarizes the trade offs in a comparison table. We observe that more modern approaches have a tendency to enhance security at the cost of usually introducing usability, cost, and scalability problems. Behavioral biometrics are vulnerable to privacy and spoofing threats; FIDO2/passkeys are simple to operate but they rely on synchronization infrastructure; and Zero-Knowledge Proofs are secure at the cost of computation. Hybrid and multi-factor designs provide the optimal tradeoff between these factors nowadays, and research in the future should enhance the possibilities of new methods of practical large scale identity systems.

Oyenike Seun Babalola, Afolayan . A. Obiniyi

The next-generation e-health systems, which include electronic health records (EHRs), telemedicine platforms, and Internet of Medical Things (IoMT) environments, need a strong access control system that protects sensitive medical data while maintaining user privacy. The conventional access control systems face security risks because of credential theft, spoofing attacks, and their reliance on centralized trust, and their inability to scale. Blockchain-enabled multimodal biometric authentication provides a secure and decentralized solution for access control in e-health systems, according to current technological advancements. This paper provides an extensive assessment of blockchain-based multimodal biometric authentication systems, which deliver privacy-protecting access control solutions for future e-health systems. The review further examines central techniques for protecting biometric templates, zero-knowledge proofs, homomorphic encryption, and secure off-chain storage systems. The research assessed existing methods by comparing efficiency for access control, ability to protect user data, capacity to handle growing user needs, ability to work with other systems, and compliance with the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) regulations. The research identifies open challenges that need resolution, which include biometric data revocability, latency constraints, cross-platform interoperability, and limited real-world deployments. The study presents upcoming research paths that will investigate lightweight blockchain systems, post-quantum cryptography, cross-chain medical identity management, and adaptive access control systems in extensive e-health environments. The review demonstrates that blockchain-based multimodal biometric authentication serves as a suitable foundation that enables secure access control through decentralized systems that protect user privacy in upcoming e-health technologies.

Khushil Godhani, Nihhar Shukla, Janam Patel, Rajesh Gupta · 5 authors

No abstract is available for this record.

Sebakara MAIC, Adria Nirere

This research addresses the critical vulnerabilities inherent in centralized identity management systems, which aresusceptible to single points of failure, data breaches, and profound privacy violations. To mitigate these risks, we propose and detailthe architectural design of a novel, decentralized identity framework that integrates blockchain technology with biometricauthentication and advanced cryptographic principles.The proposed methodology generates a unique, blockchain-based identity for each user by cryptographically hashing personal dataand biometric templates (fingerprint and facial recognition) using SHA-256. Identity verification for service providers is facilitatedby access tokens issued via smart contracts, which allow for authentication without direct access to sensitive biometric data. Thesystem enforces secure access by validating tokens against real-time biometric verification, with automatic revocation uponmismatch.The framework incorporates a Zero-Knowledge Proof (ZKP) mechanism to enable privacy-preserving verification, allowing usersto authenticate their identity while withholding the underlying data. Decentralized storage of hashed biometric templates is achievedthrough integration with the Internet Computer Protocol (ICP), thereby eliminating centralized points of failure. The system'sperformance is rigorously evaluated using key metrics, including the False Acceptance Rate (FAR), False Rejection Rate (FRR),token generation latency, and blockchain transaction throughput.This work's primary contribution is the development of a resilient, interoperable, and privacy-centric model for digital identity. Theresults demonstrate enhanced security and a reduced risk of identity theft, positioning this solution as a secure and scalablealternative to traditional centralized identity infrastructures.

Mykola Khranovskyi, Andriy Kernytskyy

The growing use of biometric authentication systems has raised serious concerns about the privacy and security of sensitive user data. Zero-Knowledge Proofs (ZKPs) provide a cryptographic solution that allows authentication without revealing the actual biometric templates. However, applying them in practice is often difficult due to the computational complexity of the circuits involved. It is commonly assumed that only simple metrics, such as Hamming distance, are suitable for these limited environments, while arithmetic metrics like Euclidean distance are considered too "heavy" or slow. This research challenges that assumption by comparing the performance of both metrics within a Groth16 Zero-Knowledge framework. For our methodology, we used a ResNet18 neural network to generate fixed-length biometric data (embeddings). To make this data compatible with the cryptographic system, we used a quantization strategy for the Euclidean metric and a binarization strategy for the Hamming metric. The experiments, conducted using the Circom compiler and snarkjs library, show very little difference in performance between the two approaches. The Euclidean circuit required 577 constraints, which is only 9% more than the 529 constraints of the Hamming circuit. Furthermore, both methods had an average proof generation time of approximately 0.5 seconds on standard hardware. These findings empirically prove that high-accuracy Euclidean comparisons can be used efficiently in Zero-Knowledge protocols. This allows developers to focus on biometric precision without sacrificing cryptographic performance.

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.