Blockchain Papers

Shreyansh Sharma, Debasis Das, Santanu Chaudhury

No abstract is available for this record.

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

Tomoki Chinen, Christopher Wiraatmaja, Yu Nakahata, Takanori Hara · 5 authors

Executing biometric matching between two embedding vectors on the blockchain remains a challenging problem due to inherent privacy concerns and the computational constraints imposed by block gas limits. To address these challenges, we propose zk-SABER, a succinct blockchain-based biometric authentication scheme that allows constant proof size and verification cost with respect to the embedding vector length. Our design combines a Merkle Tree and a biometric matching algorithm within a zkSNARK circuit to prove that a user’s biometric trait matches one of the registered templates in an anonymous manner. To ensure compatibility with state-of-the-art Deep Neural Network (DNN) models, we introduce a complete quantization pipeline that converts floating-point embeddings into zkSNARK-friendly representations. Our experiment results show constant transaction gas cost and proof size, regardless of the embedding vector length, thereby demonstrating the practicality of zk-SABER for real-world blockchain environments.

Ben-Onyeukwu, Beauty

No abstract is available for this record.

Razi Iqbal, Muhammad Afzaal, Geetanjali Rathee

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.

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.

Vaishali Kapure, Deepika Ajalkar, Arti Patle, Shibani Borde · 6 authors

Paper define innovative approach to unify authentication across Web2 and Web3 ecosystems by using biometric-driven decentralized identifiers (DIDs). The framework employs zero-knowledge attestations (ZKPs) to ensure privacy during verification processes [7], [11] and utilizes Chainlink's Cross-Chain protocol related toInteroperability(CCIP) for flawless operation across multiple blockchains [17]. To enhance liveness detection, we incorporate federated learning to eliminate centralized storage of sensitive biometric data [19]. A novel contribution is the Biometric Soulbound Token (BST), a non-transferable NFT that securely stores hashed facial data [5]. Also, quantum-resistant ZKPs are used to verify biometric matches without exposing raw inputs [14]. The DIDs function cohesively across Ethereum, Polygon, and Solana. Experimental results demon- strate a 99.2% authentication accuracy, a 1.3 -second latency, and full compliance with GDPR. By empowering users with control over their biometric data, this framework bridges centralized and decentralized platforms, enabling secure and efficient identity management.

Oleksandr Kuznetsov, Emanuele Frontoni, Kateryna Kuznetsova, Marco Arnesano · 5 authors

With the increased usage of cyber-physical systems (CPSs) in different critical domains, there is an emerging need for sound mechanisms for security and privacy. A deep learning-based biometric authentication system using feature extraction integrated with zero-knowledge proof and blockchain-based storage techniques is proposed for a secure authentication system providing better assurance in data privacy. The proposed architecture consists of three modular layers: a biometric processing layer responsible for extracting discriminative features using the FaceNet model, a cryptographic layer transforming these features into keys and generating ZKP-based proofs, and a blockchain layer for immutable authentication results storage. The system was tested on the Database of Faces, which resulted in 100% classification accuracy, reliably 194 integrated cryptographic functions through proof generation and verification times averaging 1.884 ms and 4.062 ms, respectively. Its compact proof size of 340 bytes speaks to the efficiency of the system. While these results clearly confirm the potential of such a system to be actually deployed in CPS applications, future work will address those challenges related to real-world conditions, such as diverse environments and additional biometric modalities. The proposed system enables a scalable and secure framework for CPS applications where privacy, transparency, and reliability are paramount.

Swati Kumari, Hitesh Tewari

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.

Xuechun Mao, Xiaqing Zhou, Xiaoming Zhao, Ying Chen

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.

Divesh Sarkar, Divesh Sarkar

No abstract is available for this record.

T Gomathi, S. Maflin Shaby, Saroo Raj R B, Prathap Kumar K

Body Area Network (BAN) systems ensure that physically integrated wearable and implantable biomedical devices are networked to make health monitoring possible. However, one of the major difficult issues that researchers and practicing health professionals have continued to face is protecting identified sensitive health information while at the same time addressing the consumer's right to privacy. ZHIs can offer significant benefits to assist BAN systems improve data security and privacy by enabling one party to prove the possession of certain information without revealing it to another party. This study focuses on the BAN into which ZKPs are incorporated to enhance the security of authentication, access, and data sharing among the stakeholders that include the healthcare givers and patients. Through the use of ZKPs, the proposed approach ensures that only the right people can prove the authenticity of the health data without revealing the data hence reducing on the risk of data leakage and other related issues. COVERY is specifically designed to have a low computational overhead for BAN devices by utilizing the ZKP technique. In this case, following a discussion of the proposed scheme, the simulation and real-world mode of the scheme are carried out and analyzed to determine its effectiveness and feasibility. The BAN systems enhanced through the integration of ZKP are found to enhance the data privacy of a network, decrease the attack angles and also ensure faster transfer of data securely in a healthcare network. This paper discusses how ZKPs can be adopted as a revolutionary solution for achieving privacy-preserving solutions in healthcare.

Polaki Sujatha, A Soujanya, E. J. Priyadharsini, S. Thenappan · 6 authors

This study introduces a secure voting system enhancing voter authentication, data integrity by integrating blockchain technology with an AI-driven biometric scheme, palmprint. The proposed method ensures that fraudulent voting is eliminated and allows trust within the election process in applications of e-governance because of the use of distributed ledger technology and reliable biometric verification. The system leverages the use of smart contracts to enable safe and secure transactions. It makes use of a CNN that has been trained on the PolyU Palmprint Database to check and authenticate palm prints. Simulation results show that the AI model presents phenomenal performance with 99.85% authentication accuracy and 35.42 transaction/sec blockchain free of data integrity issues. The system is also assured of an error-free user experience, excellent scalability, and lower latency. The proposed framework presents an excellent solution towards addressing the apprehensions regarding security and efficacy about the digital voting system that promotes safe and translucent e-governance.

Attaullah Buriro

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.

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.

Zibin Lin, Taotao Wang, Junhao Lai, Shengli Zhang · 6 authors

No abstract is available for this record.

Ahcène Bounceur

No abstract is available for this record.

Youngmin Jo, Sangjin Lee

이더리움은 비트코인과 함께 블록체인 기술을 기반으로 한 가상자산으로 범죄수익의 은닉 및 세탁 등 자금추적을 어렵게 하는 수단으로 사용되고 있다. 또한, 비트코인에 비해 프로그래밍이 유연하여 이더리움 기반 토큰, NFT, DeFi 등 여러 방식으로 활용되고 있다. 이러한 방식 또한 범죄수익의 은닉 수단으로 활용되고 있는 점을 고려하였을 때, 비트코인 이용 범죄에 대한 추적 방식 외에 이더리움을 이용한 범죄의 추적 방식 또한 필요성이 요구된다.<br/><br/> 본 연구에서는 이더리움 기반 토큰 중 하나인 ERC-20의 특징과 거래 유형을 분석하고 범죄에 사용된 실증사례를 소개하면서 이를 분석 및 추적할 수 있는 도구들의 방법을 소개하고 기존의 방식과 비교하여 토큰 거래 분석이 어려운 이유와 함께 해결 방식을 모색하고자 한다. 추가로, 위험성이 확인된 지갑주소를 스마트폰에서 식별할 수 있는 방식을 소개하면서 이더리움 블록체인 거래를 토대로 관련 자금 추적 방안과 위험주소 식별 방법을 함께 제안하고자 한다.

Changsong Jiang, Chunxiang Xu, Xinfeng Dong, Kefei Chen · 5 authors

Password-based authentication is widely applied in Internet of Things (IoT). It allows IoT devices to identify users with passwords to resist unauthorized access. However, choices of weak passwords, especially popular ones, might violate users’ privacy and lead to large-scale network attacks. Collection of popular passwords among IoT devices to establish blocklists via a service provider can prevent use of weak passwords. To protect unpopular passwords during collection, existing privacy-preserving schemes rely on expensive cryptographic primitives (e.g., garbled circuits and zero-knowledge proofs), which would impose heavy communication and computation burdens on constrained devices and hinder wide deployment of these schemes. In this paper, we propose EAGER+, an efficient privacy-preserving scheme for weak password collection in IoT against perpetual leakage. EAGER+ is mainly built on secret sharing and symmetric encryption, thereby enabling lightweight computation and communication on IoT devices. In EAGER+, we conceive a password-locked encryption with conditional decryption mechanism to efficiently identify popular passwords, where a password is essentially locked under itself in the encryption to guarantee its security, and the password can be revealed from the ciphertext by the service provider only if a sufficient number of devices exploit it. The mechanism is integrated with a servers-aided password-hardening mechanism to resist offline dictionary guessing attacks. Moreover, EAGER+ uses a key renewal mechanism to periodically update secrets for password hardening on key servers to thwart perpetual leakage towards the secrets. We formally analyze the security of EAGER+, and conduct experimental evaluations to show that EAGER+ is more efficient than existing schemes.

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.

Shukur H. Shakir, Alharith A. Abdullah

Biometric encryption, especially those that are fingerprint-based, plays a dual, important role in identity authentication as well as in privacy protection. Considering the crucial issue of verifying voters in electronic voting systems, ensuring voter privacy and securing sensitive biometric data is considered important. We provide an innovative approach to the integration of fingerprint authentication through biometrics with zero-knowledge proof, thus serving dual purposes of robustness in identity verification while retaining confidentiality at the level of individual voter data. We have a two-node setup architecture in which one is the Prover Node, and the other is the Verifier Node. It will process the fingerprint of the voter to create a key point cryptographic hash and transmit it to the Verifier Node by using ZKP techniques that assure no revealing of sensitive information. This will be further tested rigorously through performance metrics like latency, bandwidth, CPU usage, and memory consumption. Results have shown very low latency times in relationship to both the Prover Node and the Verifier Node, very low bandwidth usage, and efficient use of CPU and memory, which underlines efficiency and capability related to resource management of the system. The integration of biometric data with ZKP not only enhances the security and privacy of electronic voting systems but also proves viable for real-world application. The findings advocate for the broader adoption of such technologies in digital democracy initiatives, potentially transforming future electoral processes.

Takayuki Suzuki, Ken Naganuma, Kaiho Fukuchi, Takatoshi Ohara

Non-Fungible Token(NFT) issued on the blockchain made adding scarcity to digital data possible. However, as NFT transactions soared, illegal use of other people's content increased. To solve this situation, this paper proposes the Proof of Origin concept, which adds authenticity to NFTs by utilizing a technology that generates the cryptographic keys required for digital signatures directly from biometric information and describes the system architecture, data structure, and implementation of this concept. Using biometric information to link NFTs to actual persons makes it possible to provide trustworthiness and high-added value to data. At the same time, the creator does not need to manage secret keys and can safely and efficiently claim the originality of the data.

Nana Kong, Zhifeng Wan, Cui Xu, Xukai Liu · 6 authors

In the Industrial Internet of Things (IIoT) environment, a multitude of sensing devices continually gather critical data. These data are indispensable for the operations and advancements across diverse industries. However, the sharing of these data poses privacy threats, with attackers exploiting channel analysis and physical device attacks to access sensitive data. To address this, we propose a privacy protection scheme that combines smart contracts (SCs), key-insulated technology, and certificateless anonymous signature (CLBS). This scheme aims to ensure data privacy during sharing and maintain user anonymity. By leveraging SCs, our scheme enables fair and automated key distribution, replacing traditional key generation centers. Key-insulated technology ensures that the signer’s key changes periodically, enhancing system stability. The security of our solution is validated through a random oracle model, and we have optimized an elliptic curve point to reduce signature length and minimize communication overhead. Our scheme outperforms other CLBS schemes in terms of computational and communication efficiency.