M Savitha Devi, Ningthoujam Chidananda Singh, Thoudam Basanta Singh
Abstract - Blockchain enabled systems are more and more adopted in healthcare for secured processing of data, but current smart contract usage in healthcare leaks private patient data on execution. The contributions of this paper are two-fold: (1) it proposes a new framework that combines ZKPs with healthcare smart contracts/transactions to achieve full privacy preservation and (2) it discusses the security, usability, and the efficiency of the framework at the same time. Our proposed framework is based on zero-knowledge proof systems zkSNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) and zkSTARKs (Zero-Knowledge Scalable Transparent Argument of Knowledge) tailored for computer on medical data without revealing effectively. We conduct extensive analysis and prototype implementation to show that our framework is able to achieve perfect privacy preservation at a 1.87% computational overhead increase with respect to standard smart contracts. The system processes over 10,000 medical records with sub-second verification times and that meet the HIPAA requirements. Experimental results in diverse healthcare applications attest to the efficacy of the approach in practice, and show the substantial gain of privacy preservation (99.8% retention rate) and computational efficiency over the state-of-art algorithms. This paper bridges the gap between blockchain’s transparency and healthcare’s privacy requirements, laying the groundwork for secure and privacy-preserving blockchain based healthcare applications. Key Words: Zero-knowledge proofs, Smart contracts, Healthcare blockchain, Privacy preservation, zkSNARKs, zkSTARKs, Medical data security, HIPAA compliance
Minjung Park, Gyuyeon Na, Soyoun Kim, Sunyoung Moon · 6 authors
Abnormal cryptocurrency transactions - such as mixing services, fraudulent transfers, and pump-and-dump operations -- pose escalating risks to financial integrity but remain notoriously difficult to detect due to class imbalance, temporal volatility, and complex network dependencies. Existing approaches are predominantly model-centric and post hoc, flagging anomalies only after they occur and thus offering limited preventive value. This paper introduces HyPV-LEAD (Hyperbolic Peak-Valley Lead-time Enabled Anomaly Detection), a data-driven early-warning framework that explicitly incorporates lead time into anomaly detection. Unlike prior methods, HyPV-LEAD integrates three innovations: (1) window-horizon modeling to guarantee actionable lead-time alerts, (2) Peak-Valley (PV) sampling to mitigate class imbalance while preserving temporal continuity, and (3) hyperbolic embedding to capture the hierarchical and scale-free properties of blockchain transaction networks. Empirical evaluation on large-scale Bitcoin transaction data demonstrates that HyPV-LEAD consistently outperforms state-of-the-art baselines, achieving a PR-AUC of 0.9624 with significant gains in precision and recall. Ablation studies further confirm that each component - PV sampling, hyperbolic embedding, and structural-temporal modeling - provides complementary benefits, with the full framework delivering the highest performance. By shifting anomaly detection from reactive classification to proactive early-warning, HyPV-LEAD establishes a robust foundation for real-time risk management, anti-money laundering (AML) compliance, and financial security in dynamic blockchain environments.
In recent years, Femtech has emerged as a growing market category dedicated to women’s health technologies. Despite its rapid expansion, this relatively new and largely unregulated sector has experienced several concerning security breaches that compromise user privacy and intimacy. To address this critical gap between innovation and protection, we propose a novel blockchain-based consent management framework specifically designed for Femtech applications. Our solution leverages distributed ledger technology and smart contracts to create a transparent, immutable system where users can granularly control access to their sensitive health data.
Online examinations that companies rely on more frequently have made traditional centralized Learning Management Systems (LMS) vulnerable to security threats and authentication problems and result manipulation issues. The place of storing examination data in a central location creates risks for intentional changes which compromises the process fairness as well as integrity. Currently deployed blockchain solutions are ineffective because they fail to deliver both economical solutions and scalable systems that work well with AI proctoring functions. This study introduces the BlockchainBased Examination Framework (BEF) as an integrated system which unites multi-LMS operation with blockchain-based safe storage along with AI-powered examination surveillance features for real-time academic dishonesty discovery. The system utilizes Ethereum together with Hyperledger Fabric and Solana blockchains to guarantee result security and activates Zero-Knowledge Proofs (ZKP) and ECDSA signatures for authentication privacy and implements AI models for live examination monitoring. A thorough examination analyzed speed and scalability and financial efficiency together to evaluate these aspects of the three platform frameworks. Solana demonstrates superior performance through its$\mathbf{6 5, 0 0 0}$Transactions Per Second along with its affordable transaction fee of $0.00025 that makes it the best scalable and efficient choice. The AI-proctoring system demonstrated a 97.8% accuracy level together with a$\mathbf{2. 2 \%}$false positive error rate which improved examination security. The research demonstrates blockchain implementation as a critical enhancement for exam security as well as transparency levels. The future project will concentrate on Ethereum Layer-2 scaling alongside deep learning improvements to AI proctoring systems for better cost reduction and flexibility.
With the growing digitalization of financial services and the exponential rise in the transfer of sensitive data across digital and decentralized networks, cybersecurity has assumed an important role in the field of financial technology (FinTech). However, conventional security paradigms are vulnerable to centralized attacks, data integrity attacks and lack of transparency in large-scale financial marketplaces. To overcome these limitations, the immediate interconnections between blockchain and security has provided a reliable solution pertaining to transaction integrity, maintaining confidentiality in the communication and the stability of the environment. We examine innovation and development of blockchain-based cybersecurity systems, cashed to be used to secure financial transactions and provide consistent data sharing. This paper presents an architecture considering the characteristics of blockchain (i.e., decentralization, immutability, cryptographic hashing, and consensus protocols), preventing unauthorized access to data or tampering of transaction data, and providing enhanced traceability and auditability to financial institutions. In addition, the paper shows how smart contracts can automate compliance and real-time anomaly detection, thereby mitigating against human error and lowering operational risk. The approach uses both qualitative and quantitative methods, and consists of the system modeling, simulation testing, and comparative analysis with a traditional system of cybersecurity. Results reveal that blockchain-based applications drastically mitigate potential threats including double-spending, man-in-the-middle and contract violation attacks. The system also has improved performance with respect to trust establishment, peer authentication, and end-to-end data security. Finally, this work helps to dialogue towards secure digital finance by introducing an effective and scalable cybersecurity countermeasure. It emphasizes the potential of blockchain technology for securing financial infrastructure and offers practical guidance for security and policy makers, developers, and organizations that are interested in adhering to the next generation of cybersecurity attributes for the decentralized finance (DeFi) environment.
M. Kamarunisha, T. Aarthi, A. Pavithra, J. Saira Banu · 6 authors
Blockchain is a secure, transparent digital ledger that reduces the need for a centralized authority to enable secure data transmission. Blockchain technology enhances security in healthcare by resolving key issues related to interoperability, privacy, and data security. Security hacks and third-party participation in medical data cause critical sharing issues with customized information on the internet, which results in security threats. To solve the issues, the method uses Lightweight Advanced Encryption Standard (LAES) method in this paper. To enhance the security, the four phases perform: block creation, key generation, data encryption & decryption, and key authentication. First, Proof of Secure Link (PoSL) is employed to create a secure and unalterable link between blocks, providing encrypted storage and transmission of medical records. Second, Random Key Generation (RKG) is used to create cryptographically secure random keys that secure sensitive health information while encrypting it. During the third stage, LAES is employed for data encryption and decryption in order to achieve robust security by utilizing symmetric encryption methods. Finally, Zero-Knowledge Proof (ZKP) is used for the verification of keys to validate secure verification of patient identity without disclosing personal information and, therefore, securing the patient's privacy while allowing access to legitimate users.
Ioannis Sfyrakis, Paolo Modesti, Lewis Golightly, Minaro Ikegima
Blockchain and smart contracts have transformed industries by automating complex processes and transactions. However, this innovation has introduced significant security concerns, potentially leading to loss of financial assets and data integrity. The focus of this research is to address these challenges by developing a tool that can enable developers and testers to detect vulnerabilities in smart contracts in an efficient and reliable way. The research contributions include an analysis of existing literature on smart contract security, along with the design and implementation of a lightweight vulnerability detection tool called LightCross. This tool runs two well-known detectors, Slither and Mythril, to analyse smart contracts. Experimental analysis was conducted using the SmartBugs curated dataset, which contains 143 vulnerable smart contracts with a total of 206 vulnerabilities. The results showed that LightCross achieves the same detection rate as SmartBugs when using the same backend detectors (Slither and Mythril) while eliminating SmartBugs’ need for a separate Docker container for each detector. Mythril detects 53% and Slither 48% of the vulnerabilities in the SmartBugs curated dataset. Furthermore, an assessment of the execution time across various vulnerability categories revealed that LightCross performs comparably to SmartBugs when using the Mythril detector, while LightCross is significantly faster when using the Slither detector. Finally, to enhance user-friendliness and relevance, LightCross presents the verification results based on OpenSCV, a state-of-the-art academic classification of smart contract vulnerabilities, aligned with the industry-standard CWE and offering improvements over the unmaintained SWC taxonomy.
Md. Rafid Haque, Sakibul Islam Munna, Sabbir Ahmed, Md. Tariqul Islam · 6 authors
Centralized version control systems (VCS) are vital for software development but pose risks of data loss and ownership disputes. While blockchain offers a decentralized alternative, existing solutions are often hindered by high latency, compromising the real-time collaboration essential for modern workflows. This study introduces a novel hybrid architecture combining the security of the Ethereum blockchain and the InterPlanetary File System (IPFS) with two key contributions: 1) Shamir's Secret Sharing (SSS) to create a trust-minimized model for key distribution, and 2) an authoritative-first, optimistic-fallback retrieval protocol utilizing a temporary middleware to decouple the user experience from blockchain confirmation delays. We implemented a full prototype and conducted a comprehensive performance evaluation on the public Sepolia testnet. Our results demonstrate that this architecture not only provides a secure, auditable, and resilient platform for source code hosting but also achieves highly competitive user-perceived performance. Our user-perceived push time reduces submission latency by up to 49% compared to a standard git push for common repository sizes, proving that a well-designed decentralized VCS can balance the core tenets of security and decentralization with the practical need for speed and efficiency.
With the increasing need to train AI models on sensitive healthcare data, Federated Learning (FL) has emerged as a decentralized approach that avoids raw data sharing. However, existing methods such as DP-FL and zkFL still suffer from high privacy leakage, computational overhead, and scalability challenges. To overcome these limitations, this study introduces ZK-FedTransformer++, a novel privacy-preserving FL framework. It integrates lightweight TinyViT transformers, zk-SNARKs for verifiable training, differential privacy for statistical protection, and heuristic client selection for robust participation The approach provides secure model updates via cryptographic proof circuits and noise-perturbed gradients. Experiments based on the RSNA Breast Cancer Detection dataset achieve 91.2% accuracy and 35% less privacy leakage. Tools utilized include PyTorch, zk-SNARK libraries, and privacy accounting protocols. In summary, ZK- FedTransformer++ is an effective privacy enhancement, accuracy improvement, and scalability solution that is a feasible solution for secure, decentralized AI applications in real-world healthcare and IoT settings.
Michael Herbert Ziegler, Mariusz Nowostawski, Basel Katt
In this literature review, we critically examine the evolving landscape of privacy in blockchain systems, with a particular focus on the differentiation of privacy attacks and protective measures across three distinct layers: the on-chain layer; the off-chain layer; and on the infrastructure, i.e., peer-to-peer network layer. In this review, we categorize prevalent privacy attacks, such as transaction tracing, data leakage, and network surveillance, highlighting their implications at each layer. In addition, we evaluate a range of protective techniques, including cryptographic methods, zero-knowledge proofs, and other privacy-preserving protocols. We explore the compatibility of these privacy techniques with existing blockchain systems. By synthesizing current research and practical implementations, our aims are to provide a comprehensive understanding of privacy challenges and solutions in blockchain environments, identify gaps, and guide future developments in privacy-enhancing technologies within the blockchain ecosystem.
Technology has priority in today&s;s global discussions, especially in discussions that address developmental deficits in Nigeria. The country sees emerging technologies such as artificial intelligence (AI), blockchain technology, and non-fungible tokens (NFTs), among others, as veritable tools that can be used for economic and social development. This approach presents both opportunities and challenges. Nigeria&s;s teeming young population is quite receptive to technological innovation and constitutes potential for innovative solutions to the country&s;s developmental challenges. However, the inadequate intellectual property (IP) framework that should have been used to attract investments and stimulate innovation stands as a challenge to the country&s;s technological potential. This concern raises an all-important question of whether or not the existing laws and enforcement apparatus are equipped to tackle the complications that have been introduced by these new technologies. This chapter analyses IP frameworks in Nigeria and their adequacies for protecting technology and innovations. While adopting the doctrinal research methodology, the research reveals that Nigeria has advanced and developed in technology such as app creation and fintech, which are now considered beacons of economic growth. However, the lack of awareness of the importance of IP is a major limiting factor. This research concludes and recommends that Nigeria&s;s stakeholders, among other things, provide a better system for the enlightenment of the protection and recognition that has already been provided by intellectual property rights (IPR) in technology.
Ethereum smart contracts hold tens of billions of USD in DeFi and NFTs, yet comprehensive security analysis remains difficult due to unverified code, proxy-based architectures, and the reliance on manual inspection of complex execution traces. Existing approaches fall into two main categories: anomaly transaction detection, which flags suspicious transactions but offers limited insight into specific attack strategies hidden in execution traces inside transactions, and code vulnerability detection, which cannot analyze unverified contracts and struggles to show how identified flaws are exploited in real incidents. As a result, analysts must still manually align transaction traces with contract code to reconstruct attack scenarios and conduct forensics. To address this gap, TraceLLM is proposed as a framework that leverages LLMs to integrate execution trace-level detection with decompiled contract code. We introduce a new anomaly execution path identification algorithm and an LLM-refined decompile tool to identify vulnerable functions and provide explicit attack paths to LLM. TraceLLM establishes the first benchmark for joint trace and contract code-driven security analysis. For comparison, proxy baselines are created by jointly transmitting the results of three representative code analysis along with raw traces to LLM. TraceLLM identifies attacker and victim addresses with 85.19\% precision and produces automated reports with 70.37\% factual precision across 27 cases with ground truth expert reports, achieving 25.93\% higher accuracy than the best baseline. Moreover, across 148 real-world Ethereum incidents, TraceLLM automatically generates reports with 66.22\% expert-verified accuracy, demonstrating strong generalizability.
The IoT has changed the way we employ data-driven automation in a big way, but it also poses big risks to users privacy and security. Blockchain technology is appealing for a number of reasons, including its decentralisation, immutability, and use of smart contracts. This article is about how blockchain technology can make Internet of Things (IoT) ecosystems safer. We provide a safe architectural foundation for the Internet of Things (IoT) using cryptographic protocols and hardware security modules (HSMs). Then, we look at the literature on the subject, point out problems, and evaluate blockchain systems that are suitable for the IoT. A prototype implementation shows that it works by using Proof of Stake (PoS) consensus and SHA-256 hashing. The study looks at performance and dependability, and it also employs simulation to test the method.
The rapid expansion of Internet of Things (IoT) deployments across smart environments introduces critical security challenges, particularly at the device identity and physical layers. Traditional cryptographic methods and Distributed Ledger Technologies (DLTs), while valuable, often fail to account for the constrained resources of IoT devices and their susceptibility to physical-layer attacks. This paper proposes a scalable, lightweight security framework that integrates Physical Unclonable Functions (PUFs), Zero-Knowledge Proofs (ZKPs), and a permissioned blockchain to establish end-to-end trust in distributed IoT ecosystems. PUFs act as hardware-rooted trust anchors, enabling secure key generation and unclonable device identity without relying on non-volatile memory. ZKPs facilitate mutual authentication by allowing devices to prove legitimacy without revealing any identifying information. A permissioned blockchain acts as a decentralised verification and audit layer, immutably recording authentication events and ensuring tamper resistance with controlled governance. The proposed architecture is designed to counteract physical tampering, spoofing, and identity forgery while remaining computationally viable for resource-constrained IoT devices. This work presents the foundation for a robust, privacy-preserving, and decentralised security model, bridging the gap between hardware-level assurance and scalable trust in future IoT deployments.
Physical Unclonable Functions (PUFs) and Hardware Security
T. Ratha Jeyalakshmi, Alamma Bh, H S Harshitha, K. Agarwal R.
Privacy of users and security of data are important issues that will be exposed to use in the Metaverse by use of Digital Twins (DTs). The current paper suggests a privacy-preserving system, which combines Zero-Knowledge Proofs (ZKPs) of secure identity verification and Federated Learning (FL) of decentralized model training. The framework allows for alleviating the risk of storing data in central facilities and preventing unauthorized access by locally processing data and using cryptographic solutions. The results produced by the evaluation prove that the proposed system is capable of attaining the necessary level of privacy of its users and ensuring reliable and scalable communications within the Metaverse applications.
Aleksandr Kormiltsyn, Sowelu Avanzo, Vimal Dwivedi, Alex Norta · 5 authors
This paper explores conflict resolution in decentralized e-health prescription creation workflows, necessary for secure and efficient multi-stakeholder data sharing. Consensus mechanisms ensure consistency and enable trust across distributed systems. Decentralized Autonomous Organizations (DAOs) are adopted for decentralized decision-making in several domains but remain unexplored in e-healthcare. Current consensus mechanisms lack integration with governance models, limiting their adaptability to domain-specific requirements. Moreover, there is no existing consensus algorithm adapted for e-health, resulting in the lack of privacy, interoperability, and patient-centered data ownership. As a result, automatic conflict resolution in interorganizational e-health processes is complicated or almost impossible. To address this gap, we propose a domain-specific consensus algorithm adapted to the requirements of the e-health domain. The algorithm is embedded within a DAO-based governance framework, enabling transparency in decision-making among e-health stakeholders. Such integration enables automated, privacy-preserving conflict resolution in interorganizational e-health workflows. Following the Design Science methodology, the consensus algorithm for e-health DAO is based on stakeholder-driven requirements and evaluated using Colored Petri Nets (CPN). The evaluation shows the solution improves conflict resolution enabling fair, efficient, and privacy-aware collaboration in decentralized e-health.
The looming occurrence of large scale quantum computing presents a significant danger to the mathematical basis of cryptography that forms the foundation of the currently-deployed blockchain networks, particularly those networks that operate within distributed cloud-based systems. Existing blockchain models based on traditional cryptographic algorithms like RSA and ECC can be broken using quantum computing, thus new models built around the concept of quantum-resistance should be developed. This article proposes a new framework of Post Quantum Secure Blockchain that combines enhanced lattice-based cryptography with more computation time consensus protocol, especially designed to work effectively in cloud-based systems. The suggested architecture utilizes NTRU and Ring-LWE lattice primitives to sign transactions, verify them and to certify the integrity of the block such that transactions and block cannot be corrupted, even by a quantum adversary. To resolve the scalability and energy consumption challenges, a dynamically adjusted time based on consensus of the computing - extends Trusted Execution Environments (TEEs) to support dynamically adaptive block proposal timings depending on real time network conditions in terms of latency, throughput, and node workload. The system is also using lightweight hash chain validation to further simplify computational complexity, and to enhance the transaction throughput. Results gained in the experimental studies within the setup emulating a cloud context show that with the proposed model, the transaction latency and energy consumption scales down considerably in contrast to the traditional Proof-of-Work and Proof-of-Stake systems without jeopardising security assurances. The given research builds a scalable, future-ready blockchain framework that can be used in next-generation decentralized applications in the fields of finance, healthcare, and IoT, overcoming the quantum-security and high-performance barrier between quantum security and real-world, high-performance cloud deployment.
Purpose Previous research has identified numerous business applications for non-fungible tokens (NFTs), yet there is a dearth of studies exploring this phenomenon in managerial practice. This article fills this gap by employing service-dominant logic and examining multiple NFT projects to gain insights into how the deployment of NFTs can contribute to achieving companies’ strategic objectives. Design/methodology/approach Our research employs a diverse case selection strategy, focusing on 13 well-established brands using NFTs to transform their activities across various countries and sectors. We conducted a secondary data analysis, including press articles and social media content, to identify relevant NFT projects and develop five propositions that offer entry points for future studies in the broad field of crypto-marketing. Findings Five main areas are identified in which NFTs can help advance companies’ strategic objectives: (1) enhancing perceived quality, (2) driving innovation performance, (3) accessing funds with zero interest, (4) enhancing flexibility and (5) promoting sustainability. Originality/value This article is among the first to investigate and systematize current trends and applications of NFTs in relation to companies’ strategic objectives. It provides actionable insights for practitioners and specific propositions for further research by management scholars.