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.
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
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.
Abstract Traditional Identity and Access Management (IAM) systems rely on static credentials and centralized authorities, leaving organizations vulnerable to single points of failure, credential theft, insider misuse, and increasingly sophisticated deepfake impersonation attacks. In this paper, we propose a Decentralized AI-powered Zero-Trust IAM (DAZT-IAM) framework that combines permissioned blockchain infrastructure, self-sovereign identity (SSI) principles, and deepfake-resistant multimodal biometric authentication (face, voice, and behavioral keystroke dynamics) with a continuous, risk-adaptive AI trust scoring engine. The proposed system is compliant with ZTA principles and checks every access request continuously, unlike the older “authenticate-once” models. Blockchain-anchored Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) eliminate the dependency on a central identity provider. The biometric pipeline includes a dedicated deepfake-detection module that employs frequency-domain artifact analysis and temporal consistency checks to counteract synthetic media spoofing. We describe the system architecture, consensus and smart-contract design, the multi-modal fusion and liveness detection pipeline, and a risk-scoring model for adaptive access decisions. The experimental evaluation on simulated and benchmark datasets demonstrates that the proposed framework provides competitive authentication accuracy, high detection rates of deepfake attacks, and low average access decision latency, while removing single points of failure for identity. Our results demonstrate that the integration of blockchain-based decentralization and AI-based continuous trust evaluation provides a pragmatic approach of resilient and privacy-preserving IAM for sustainable digital infrastructure.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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
The rapid adoption of Internet of Things (IoT) in Healthcare has significantly enhanced real-time patient monitoring and decision making. However, security and privacy still remain the major concern due to sensitive medical data of patients especially on low-power IoT devices. Traditional authentication schemes like Zero Knowledge Proof (ZKP) and Elliptic Curve Cryptography (ECC) often struggle with efficiency in resource-constraint environments due to their computational overhead. In order to address these challenges, we propose a Neural-Based Hybrid and Adaptive Framework that combines Schnorr ZKP with Kyber-based key encapsulation, using a neural network to dynamically select Kyber variants (512, 768, 1024) based on device parameters (type, authentication time, transmission time) to balance security and efficiency for low-power IoT devices. Extensive experiments validated robust security against replay and spoofing attacks, achieving authentication success for legitimate clients and zero attack successes. Furthermore, our proposed framework outperforms traditional Kyber1024 and ZKP/ECC based authentication schemes in terms of authentication time and computational overhead making it robust and scalable solution for sensitive and resource-limited environments like HealthCare IoT systems. • Design a novel hybrid authentication framework that integrates ZKP and Kyber PQC (Post Quantum Cryptography) to ensure secure and efficient authentication for IoT healthcare devices. • Utilize Neural Network to intelligently select the most appropriate Kyber variant based on threat level and computational efficiency. • Provide comprehensive experimental analysis comparing the proposed hybrid and adaptive framework with traditional ZKP, ECC and static Kyber implementations.
Patients and healthcare authorities frequently lack confidence in one another when it comes to the security of their medical records in healthcare settings. Particularly when it comes to patient data management, hospitals are infamous for having inadequate security and have long been the target of cyberattacks. Using blockchain technology to store medical records has drawbacks, including an excessive dependence on centralised cloud servers for key storage, privacy concerns and the potential for attackers to deduce personal information about patients based on their blockchain activity. A system where patients have autonomy over their medical records and who can view them is a promising scenario. This article provides a framework for indexing and securing a user’s medical records, with emphasis placed on the healthcare setting using an Ethereum blockchain. The records are secured using biometric authentication and the patient’s Personal Identifiable Information (PII). The patient can grant and revoke access to their records to individual healthcare authorities, and the Interplanetary Name System (IPNS) is used for off-chain record storage. The framework is modular and can be adapted for use in other environments, such as proof of ownership of tickets, and storing travel documents for verification by border control. A smart contract is used to store the hashes of the patient’s iris scans on an Ethereum Virtual Machine (EVM) compatible blockchain. Privacy-preserving identifiers are used to anonymise the patient and where their records are stored on the blockchain. Our approach is to the best of our knowledge the only one that simultaneously offers encryption, anonymity, unlinkability and efficient off-chain storage. Additionally, our approach is the only approach we are aware of that provides record revocability.
Daniël Reijsbergen, Eyasu Getahun Chekole, Howard Halim, Jianying Zhou
Biometric authentication relies on physiological or behavioral traits that are inherent to a user, making them difficult to lose, forge or forget. Biometric data with a temporal component enable the following authentication protocol: recent readings of the underlying biometrics are encoded as time series and compared to a set of base readings. If the distance between the new readings and the base readings falls within an acceptable threshold, then the user is successfully authenticated. Various methods exist for comparing time series data, such as Dynamic Time Warping (DTW) and the Time Warp Edit Distance (TWED), each offering advantages and drawbacks depending on the context. Moreover, many of these techniques do not inherently preserve privacy, which is a critical consideration in biometric authentication due to the complexity of resetting biometric credentials. In this work, we propose ZK-SERIES to provide privacy and efficiency to a broad spectrum of time series-based authentication protocols. ZK-SERIES uses the same building blocks, i.e., zero-knowledge multiplication proofs and efficiently batched range proofs, to ensure consistency across all protocols. Furthermore, it is optimized for compatibility with low-capacity devices such as smartphones. To assess the effectiveness of our proposed technique, we primarily focus on two case studies for biometric authentication: shake-based and blow-based authentication. To demonstrate ZK-SERIES's practical applicability even in older and less powerful smartphones, we conduct experiments on a 5-year-old low-spec smartphone using real data for two case studies alongside scalability assessments using artificial data. Our experimental results indicate that the privacy-preserving authentication protocol can be completed within 1.3 seconds on older devices.
The widespread adoption of e-health systems raises critical concerns regarding data privacy and network security. Ensuring secure and reliable data sharing between patients and healthcare professionals remains a significant challenge. To address this, we propose a novel anonymous authentication scheme tailored for e-health environments, integrating zero-knowledge proof (ZKP) with multimodal biometrics. Our key contributions are as follows: (1) applying the Pedersen vector commitment algorithm to construct a biometric-based ZKP scheme, thereby ensuring enhanced security and privacy-preserving authentication; (2) utilizing multimodal cancelable biometrics generate (MCBG) technology, integrating fingerprint, face, and iris modalities to strengthen the security of the verification process; and (3) providing a detailed security analysis that demonstrates our scheme meets essential security requirements, including anonymity, authenticity, unlinkability, forward security, and resistance to replay attacks. Experimental results demonstrate stable proving and verification time of approximately 78 ms and 140 ms, respectively, regardless of the proof range, validating its efficiency and practicality for secure authentication in e-health systems.
This paper presents a comprehensive review of the technological advancements, practical applications, inherent challenges, and emerging trends shaping the field of mobile biometrics. Over the past decade, the domain has evolved from basic fingerprint sensors to sophisticated multimodal systems leveraging AI-driven physiological and behavioral biometrics. The analysis examines the vast opportunities in finance, health-care, and digital identity management, while emphasizing the critical need to address privacy, security, regulatory, and ethical concerns. Furthermore, the study underscores the importance of collaborative efforts, highlighting promising future directions such as decentralized biometric storage and blockchain integration to enable secure and user-centric mobile experiences.