The explosive gains of FinTech-enabled digital payments have raised concerns about transaction privacy, the leaking of identity information and regulatory compliance on public blockchains. Existing privacy-preserving payment solutions either have a high computational overhead or do not offer controlled auditability as demanded by financial regulators. This research addresses the problem of ensuring good privacy of transactions while guaranteeing lawful transparency of decentralized payment systems. The goal is to create a blockchain payment framework that incorporates zero-knowledge proof (ZKP) which will ensure payer anonymity, transaction unlinkability and selective regulatory disclosure. The proposed method combines cryptographic identity commitments, private transaction circuits with zk-SNARK and gas optimized smart contract verification with controlled audit proofs. The framework is tested on the Private-FinPay data set that contains two million transactions. Experimental results indicate that the proposed ZKP-FinPay can achieve$\mathbf{1, 2 0 0}$transactions per second, verification latency of$\mathbf{1 2 0}$ms, anonymity set of 50,000 users, and 0.5% probability of privacy leakage, which is better than the five state-of-the-art privacypreserving payment protocols. These findings confirm the viability of regulatory-compliant privacy preservation of FinTech blockchain systems as being technically feasible and practically scalable.
G. Sreenivasulu, Bathula Siva Nageswara Rao, C. Rama Krishna, Srikanth Lukka ¡ 6 authors
This study presents a comprehensive comparative research of the significant blockchain consensus algorithms, such as Proof of Work, Proof of Stake, Delegated Proof of Stake, Practical Byzantine Fault Tolerance, Proof of Authority, and hybrid mechanisms, and the purpose of it is to assess the performance, safety and applicability of blockchain to the modern use of blockchain. The studies analyze the key metrics, including throughput, latency, resource consumption, finality behavior, and fault tolerance in various network conditions based on a quantitative, simulation-based methodology with the help of secondary datasets. The results indicate that there are high levels of performance differences among consensus algorithms, as permissioned and delegated algorithms show better efficiency, and low latency, whereas public mechanisms put more emphasis on decentralization, disregarding speed and energy efficiency. The paper identifies the trade-offs inherent to consensus design and points out that no one mechanism is optimal, instead it needs to be chosen based on applicationspecific factors to do with scalability, trust, security, and decentralization. These insights help gain better insight into the issue of consensus behavior and make future choices regarding building the blockchain system.
This paper introduces a robust, scalable, and flexible workflow architecture designed to overcome longstanding challenges in the financial services sector. Financial institutions often face operational bottlenecks due to fragmented legacy systems, redundant Know Your Customer (KYC) procedures, and manual, error-prone processes. To address these inefficiencies, we propose a secure, distributed architecture leveraging the Distributed Ledger Technology (DLT) of R3 Corda to ensure immutable, auditable, and tamper-resistant records. The system integrates RESTful APIs to abstract the underlying blockchain complexity, providing seamless interoperability between banking systems and third-party services. Key modules include automated KYC verification, loan processing, and document authentication, which collectively reduce processing times, lower operational risk, and enhance regulatory compliance. To ensure user accessibility, the system features an intuitive graphical user interface (GUI) developed with Bubble.io, creating a seamless and efficient mechanism for end-to-end loan life cycle management. The system's modular architecture is validated through extensive testing, including Cypress-based automation, and is designed for future scalability, positioning it as a forward-thinking solution for broader adoption of blockchain in digital financial services.
P. Chinnasamy, N. Hemanth Reddy, K. Manjusri, P. Kusuma Priya ¡ 5 authors
Medi Chain is a system that is established to introduce a higher degree of trust and transparency into the process of medicine supply. In the current times, counterfeit and bootleg drugs are a point of serious concern, particularly in cases where the physician prescribes them. Medicine batches are tracked and recorded by Medi Chain via blockchain since the stage of manufacture to the point of sale. The data stored is unchangeable and unbreachable as all the data is stored on the Sepolia Ethereum network in the form of smart contracts. Through QR- based checks, the platform enables users of the site to scan a code and instantly access essential information, including the source of the medication, who touched it, and the presence of a valid prescription pivotal to that prescription. This has made the system easy to use as the blockchain logic is written in Solidity and Web3.js, and the user interface is written in React.js and Tailwind CSS. Firebase is used to deal with secure log in, document uploading, and storage of prescription files. The system includes some basic yet significant precautions to ensure that the process of medicine handling happens safely in all stages. Each medicine flow remains exposed and safe and cannot be abused or misused. MetaMask logins, quick scans on QR, allow its users to quickly verify authenticity, making sure that prescriptions are adhered to.
This article examines the probative value of blockchain (distributed ledger technology) in legal proof. It highlights the technologyâs core featuresâdecentralization, immutability, cryptography, and time-stampingâand assesses how they fit within rules of evidence, particularly the requirements of electronic writing and electronic signature. The study also discusses the extent of legislative recognition in Morocco and comparative systems, with a focus on identity attribution and the link between a digital record and its author. It concludes that blockchain records may carry increasing persuasive force, while full evidentiary equivalence requires clearer regulatory frameworks and trusted digital services to ensure integrity and reliability.
This paper proposes a Decentralized Autonomous Intelligence (DAI) architecture that overcomes the self-referential limitations of conventional AI and Web3 systems by dynamically grounding collective intelligence in physical reality. By coupling internal consensus with high-fidelity external data such as environmental, biological, and economic signals, the framework prevents value drift, Sybil manipulation, and speculative bias. The result is a reality-aligned, secure, and scalable intelligence system optimized for real-world utility and immediate deployment.
The pharmaceutical industry plays an important role in protecting community health by researching, developing and distributing drugs to prevent and cure illnesses. As an integral part of healthcare industry, it faces several challenges such as rising research and development costs, extended approval timelines, supply chain inefficiencies and low patient involvement. This paper examines role of decentralized autonomous organizations (DAOs) in addressing these challenges. It reviews DAO frameworks, decision-making models, reward mechanism, and roles of stakeholders using case studies such as VitaDAO and Molecule to explain their functioning and adoption of DAOs in pharmaceutical industry. DAOs offer a promising alternative to traditional hierarchical systems by promoting innovation and empowering stakeholders. To advance drug discovery and development, DAOs provide a shared platform for scientists, patients, funding agencies and regulatory authorities to work in a democratic and collaborative way to make decisions and manage operations in drug industry. Despite its several benefits, DAOs also face significant challenges, including regulatory uncertainty, data protection, and ensuring longterm sustainability. Future directions include integration of AI into pharmaceutical DAOs, privacy-enhanced DAOs, and cross-DAO cooperation to promote global collaboration across borders.
The subject of the stud y is a digital token in a crossâborder payment infrastructure (hereinafter referred to as CBPI) based on distributed ledger technology (hereinafter referred to as DLT). The purpose of the work is to analyze and scientifically evaluate methodological approaches to the formation of CBPI. The relevance of the work is due to the atmosphere of uncertainty and growing risks of external impact on the cross-border payment infrastructure that the Russian Federation has faced in recent years, as well as the need to address the challenge of ensuring accessibility, continuity, sustainability and security of its operation. As a result of the research, using heterodox, systemic, structural-functional, cybernetic, pragmatic and institutional approaches, the economic characteristics of the payment token have been developed and presented, including the most significant ones for the smooth implementation of cross-border payment transactions. It is concluded that the existing approaches make it possible to determine the main economic characteristics of a digital token in a cross-border payment infrastructure based on DLT, including security, cost stability, liquidity, volatility, as well as auxiliary ones â interoperability, scalability, transactional neutrality, economic isolation.
Daniel Qian, Xiyu Hao, Jinkun Geng, Yuncheng Yao ¡ 7 authors
As Byzantine Fault Tolerant (BFT) protocols are increasingly adopted for user-facing applications such as payments and smart contracts, it is crucial that they provide low latency. To reduce latency, some BFT consensus protocols use a leaderless, speculative, fast path where clients broadcast requests directly to replicas, enabling end-to-end commit latency of two message delays ($2Î$). However, such a fast path is extremely fragile: concurrent requests can cause replicas to diverge when they receive requests in different orders, triggering costly recovery procedures. This paper presents Aspen, a leaderless speculative BFT protocol that handles concurrent requests while achieving near-optimal latency of $2Î+ Îľ$. The $Îľ$ term is a short waiting delay introduced by Aspen's best effort ordering layer, which uses loosely synchronized clocks and network delay estimates to provide a tentative order. To make its fast path even more robust to intermittent divergence, Aspen adds extra replicas ($n = 3f + 2p + 1$) as well as novel recovery mechanisms that allow the system to tolerate divergence while preserving safety and performance. In experiments with geo-distributed replicas, Aspen reduces the median latency of requests by $1.1\times$--$3.8\times$ compared to state-of-the-art BFT protocols, while sustaining up to $0.75\times$ the peak throughput of throughput-optimized designs.
Martin PereĹĄĂni, TomĂĄĹĄ HladkĂ˝, Jakub KubĂk, Ivan Homoliak
The aim of this work is to enhance blockchain security by deepening the understanding of selfish mining attacks in various consensus protocols, especially the ones that have the potential to mitigate selfish mining. Previous research was mainly focused on a particular protocol with a single selfish miner, while only limited studies have been conducted on two or more attackers. To address this gap, we proposed a stochastic simulation framework that enables analysis of selfish mining with multiple attackers across various consensus protocols. We created the model of Proof-of-Work (PoW) Nakamoto consensus (serving as the baseline) as well as models of two additional consensus protocols designed to mitigate selfish mining: Fruitchain and Strongchain. Using our framework, thresholds reported in the literature were verified, and several novel thresholds were discovered for 2 and more attackers. We made the source code of our framework available, enabling researchers to evaluate any newly added protocol with one or more selfish miners and cross-compare it with already modeled protocols.
Trust between entities in any scenario without a trusted third party is very difficult, and trust is exactly what blockchain aims to bring into the digital world with its basic features. Many applications are moving to blockchain adoption, enabling users to work in a trustworthy manner. The early generations of blockchain have a problem; they cannot share information with other blockchains. As more and more entities move their applications to the blockchain, they generate large volumes of data, and as applications have become more complex, sharing information between different blockchains has become a necessity. This has led to the research and development of interoperable solutions allowing blockchains to connect together. This paper discusses a few blockchain platforms that provide interoperable solutions, emphasising their ability to connect heterogeneous blockchains. It also discusses a case study scenario to illustrate the importance and benefits of using interoperable solutions. We also present a few topics that need to be solved in the realm of interoperability.
Xunqiang Lan, Xiao Tang, Ruonan Zhang, Bin Li ¡ 7 authors
Blockchain plays a crucial role in ensuring the security and integrity of decentralized systems, with the proof-of-work (PoW) mechanism being fundamental for achieving distributed consensus. As PoW blockchains see broader adoption, an increasingly diverse set of miners with varying computing capabilities participate in the network. In this paper, we consider the PoW blockchain mining, where the miners are associated with resource uncertainties. To characterize the uncertainty computing resources at different mining participants, we establish an ambiguous set representing uncertainty of resource distributions. Then, the networked mining is formulated as a non-cooperative game, where distributionally robust performance is calculated for each individual miner to tackle the resource uncertainties. We prove the existence of the equilibrium of the distributionally robust mining game. To derive the equilibrium, we propose the conditional value-at-risk (CVaR)-based reinterpretation of the best response of each miner. We then solve the individual strategy with alternating optimization, which facilitates the iteration among miners towards the game equilibrium. Furthermore, we consider the case that the ambiguity of resource distribution reduces to Gaussian distribution and the case that another uncertainties vanish, and then characterize the properties of the equilibrium therein along with a distributed algorithm to achieve the equilibrium. Simulation results show that the proposed approaches effectively converge to the equilibrium, and effectively tackle the uncertainties in blockchain mining to achieve a robust performance guarantee.
Learning and Employment Record (LER) systems are emerging as critical infrastructure for securely compiling and sharing educational and work achievements. Existing blockchain-based platforms leverage verifiable credentials but typically lack automated skill-credential generation and the ability to incorporate unstructured evidence of learning. In this paper,a privacy-preserving, AI-enabled decentralized LER system is proposed to address these gaps. Digitally signed transcripts from educational institutions are accepted, and verifiable self-issued skill credentials are derived inside a trusted execution environment (TEE) by a natural language processing pipeline that analyzes formal records (e.g., transcripts, syllabi) and informal artifacts. All verification and job-skill matching are performed inside the enclave with selective disclosure, so raw credentials and private keys remain enclave-confined. Job matching relies solely on attested skill vectors and is invariant to non-skill resume fields, thereby reducing opportunities for screening bias.The NLP component was evaluated on sample learner data; the mapping follows the validated Syllabus-to-O*NET methodology,and a stability test across repeated runs observed <5% variance in top-ranked skills. Formal security statements and proof sketches are provided showing that derived credentials are unforgeable and that sensitive information remains confidential. The proposed system thus supports secure education and employment credentialing, robust transcript verification,and automated, privacy-preserving skill extraction within a decentralized framework.
Systematic reviews are essential for evidence-based medicine, but reviewing 1.5 million+ annual publications manually is infeasible. Current AI approaches suffer from hallucinations in systematic review tasks, with studies reporting rates ranging from 28--40% for earlier models to 2--15% for modern implementations which is unacceptable when errors impact patient care. We present a causal graph-enhanced retrieval-augmented generation system integrating explicit causal reasoning with dual-level knowledge graphs. Our approach enforces evidence-first protocols where every causal claim traces to retrieved literature and automatically generates directed acyclic graphs visualizing intervention-outcome pathways. Evaluation on 234 dementia exercise abstracts shows CausalAgent achieves 95% accuracy, 100% retrieval success, and zero hallucinations versus 34% accuracy and 10% hallucinations for baseline AI. Automatic causal graphs enable explicit mechanism modeling, visual synthesis, and enhanced interpretability. While this proof-of-concept evaluation used ten questions focused on dementia exercise research, the architectural approach demonstrates transferable principles for trustworthy medical AI and causal reasoning's potential for high-stakes healthcare.
Open access
2 source records
Machine Learning in Healthcare
Explainable Artificial Intelligence (XAI)
Artificial Intelligence in Healthcare and Education
Financial transactions demand exceptionally robust security, especially in light of the rapid advancement of quantum computing, which poses a severe threat to classical cryptographic mechanisms used in modern banking systems. Among various financial operations, transaction processing remains the most critical and vulnerable component. To address this emerging challenge, we introduce a Distributed Ledger Technology (DLT)-based secure framework for quantum-resistant financial transactions. The proposed architecture leverages lattice-based cryptographic security to ensure resilience against quantum attacks while preserving essential security attributes such as privacy, accountability, and data integrity. Furthermore, to demonstrate its effectiveness, the proposed framework is also compared with existing solutions in the literature.
Cryptocurrency has been legalized in the United States. The 2025 GENIUS Act provides a sensible regulatory framework for cryptocurrency as a medium of exchange while avoiding predicted monetary chaos. The Act correctly distinguishes between volatile fiat cryptocurrencies like Bitcoin, which are unsuitable as payment mechanisms, and stablecoins backed by traditional currencies and reserves. Historical analysis spanning American monetary systems from the âfree banking eraâ before 1860 through modern digital payments demonstrates that private money has long coexisted with government currency but requires appropriate regulation to prevent bank runs, fraud, and monetary instability. The GENIUS Act addresses key cryptocurrency risksâtechnological failures, volatility, issuer fraud, and loss of monetary controlâthrough one-to-one reserve requirements, business activity restrictions, supervisory oversight, and priority for stablecoin holders in bankruptcy proceedings. Critics argue the Act enables monetary chaos, lacks consumer protections, and permits âBig Techâ to control money supply. These criticisms are largely unfounded. The Actâs reserve requirements prevent fractional banking and money supply disruption, while existing corporate governance and consumer protection mechanisms provide adequate safeguards. The GENIUS Act represents neither a return to nineteenth-century monetary chaos nor abandonment of oversight, but rather a measured approach distinguishing payment uses from investment uses of cryptocurrency. Success ultimately depends on whether stablecoins can satisfy the âno-questions-askedâ principle and offer competitive transaction costs compared to existing payment systems like credit cards and digital wallets.
Nandhini S, Hrithik M, Kamalesh S, Aswin C ¡ 6 authors
ABSTRACT: Centralized digital marketplaces dominate todayâs online commerce but suffer from inherent limitations such as single points of failure, lack of transparency, data monopolization, and trust dependency on intermediaries. To address these challenges, this paper presents the design and implementation of a decentralized marketplace built on blockchain technology. The proposed system enables peer-to-peer trading without the involvement of centralized authorities, ensuring transparency, security, and fairness among participants. Smart contracts are employed to automate transactions, enforce business rules, and eliminate the need for trusted third parties. Distributed ledger technology ensures immutability of records, while cryptographic mechanisms provide secure identity management and transaction validation. The marketplace supports secure listings, decentralized payments, dispute resistance, and trustless execution, thereby reducing operational costs and increasing user autonomy. Experimental analysis demonstrates improved reliability, resistance to tampering, and enhanced trust compared to traditional centralized platforms. The proposed decentralized marketplace framework highlights the potential of blockchain-based systems in redefining digital commerce by promoting transparency, decentralization, and user empowerment. Keywords: Decentralized Marketplace, Blockchain Technology, Smart Contracts, Peer-to-Peer Trading, Distributed Ledger, Trustless Transactions, Cryptographic Security, Transparency, Digital Commerce, Disintermediation.
Blockchain ecosystems, particularly decentralized finance (DeFi) platforms, have become high-value targets for adversaries exploiting vulnerabilities in smart contracts. Traditional honeypots designed to attract such attackers are often static and easily detectable once adversaries recognize repeating patterns. In this paper, we propose Adaptive AI-Powered Smart Contract Honeypots (AASCH), a novel deception-based security mechanism that dynamically evolves its behavior using reinforcement learning techniques. The honeypot continuously adapts to attacker strategies by modifying contract responses, transaction logic, and resource interactions in real time, thereby creating an unpredictable deception environment. We implement AASCH on the Ethereum test network and simulate various exploit scenarios, including reentrancy attacks, flash-loan exploits, and oracle manipulations. Experimental results demonstrate that AASCH significantly improves attacker capture rates (up to 37% higher than static honeypots) while reducing evasion attempts and false positives. Our findings indicate that adaptive deception is a promising defense strategy for strengthening blockchain ecosystems against evolving cyber threats.
Blockchain technology offers decentralized and secure transaction processing but suffers from critical limitations in scalability, energy efficiency, and latency, hindering its adoption in real-time high-throughput applications. This study proposes a novel Adaptive Global BestâWorst Particle Swarm Optimization (AGBWPSO) algorithm integrated with dynamic sharding to address these challenges effectively. Unlike traditional GBWPSO, the proposed AGBWPSO employs a dual-extremum influence mechanism that combines both global best and worst positions, along with adaptive nonlinear parameter adjustment strategies for the inertia weight, cognitive, and social coefficients. This enhances explorationâexploitation balance, prevents premature convergence, and ensures efficient shard reallocation under dynamic transaction loads. The integration with dynamic sharding enables parallel transaction processing across optimally configured shards, significantly improving blockchain performance metrics. Extensive simulations conducted on Ethereum, Bitcoin, Hyperledger Fabric, financial, and IoT transaction datasets demonstrate that the proposed AGBWPSO achieves up to 5.88% improvement in transaction throughput (TPS), 14.3% reduction in latency, and 20% reduction in energy consumption per transaction compared to existing optimization methods. These results establish AGBWPSO as a robust and scalable solution for enhancing the operational efficiency and sustainability of blockchain networks in real-world applications.
Evidence management comes with requirements of a visibly secure, immutable, and scalable system to drive legal proceedings with ethicacy. Where research on fully on-chain solutions shows unrealistic and extravagant costs and performance limits, the traditional off-chain centralized storage systems exhibit an insecure environment, poor traceability, and tampering concerns. ChainSEAL is a hybrid Blockchain - IPFS-based forensic Evidence Management platform that integrates IPFS for encrypted Evidence file storage, blockchain as a distributed ledger for File hash and metadata, while off-chain storage for key management. The methodology explains the system flow, that as the evidence is submitted, the FIR is generated, the case request is created, and the evidence cycle is initiated. The cycle starts with fetching the SHA-256 of the file, then encrypting the evidence, submitting it on IPFS, fetching the Content Identifier (CID) of the file on IPFS, uploading the CID + File Hash + Metadata on-chain with a maintained verifiable Chain of Custody of the Evidence cycle. This ensures confidentiality and immutability of the system. The proposed framework is empirically evaluated for cost, storage efficiency, latency, and tamper-proofness. Its legal admissibility is established through an analysis of immutability, chain of custody integrity, and role-based access control.
The urban administration in Pakistan has transformed as a result of political and economic shifts. The urban government in Pakistan has been influenced by external financing, which is a reflection of institutional reforms, fiscal decentralization, and the priorities of global development. Over the course of the last three decades, Pakistan's urban management has transitioned from a centralized bureaucratic authority to fragmented local governance systems that are shaped by donor-driven projects and conditional cash inflows. An in-depth analysis of how multilateral development banks and bilateral aid influence urban policy, infrastructure, and service delivery is presented in this specific piece of writing. The evidence demonstrates that the use of external financing has hastened the process of urban modernization while simultaneously exacerbating governance problems such as policy incoherence, accountability deficiencies, and socio-spatial inequities. In this study, political economics research and urban planning perspectives are combined in order to investigate how external funding mechanisms influence the capacities of local governments and the transformation of urban infrastructure in Pakistan's fast-growing cities. The findings highlight the necessity of having governance structures that are adaptable and, in a position, to strike a balance between local interests and global urban finance strategy.
Bambang Leo Handoko, Arta Moro Sundjaja, Evelyn Hendriana
The rapid rise in cryptocurrency presents both opportunities and challenges for retail investors due to its volatility and technological complexity. Research on investment decisions has primarily focused on behavioural finance, often overlooking how learning and literacy shape investor actions. This study addresses this gap by examining how herding behaviour, financial literacy, and digital literacy impact cryptocurrency investment decisions. Grounded in Social Learning Theory and supported by UTAUT to operationalise digital literacy, this study examines how herding behaviour, financial literacy, and digital literacy shape cryptocurrency investment decisions. We analyse survey data from 138 Indonesian retail investors through PLS-SEM. Key findings show that financial literacy (β = 0.443, t = 5.041) and digital literacy (β = 0.495, t = 4.246) are primary determinants of investment decisions, while herding behaviour (β = 0.016, t = 0.628) does not directly influence them but does so indirectly by enhancing investor literacy. This demonstrates that social observation and learning can convert herd-driven impulses into rational choices when mediated by literacy. By extending Social Learning Theory into digital investment contexts, this study provides insights for investors and policymakers seeking to enhance financial and digital literacy.
This study systematically examines the transformative role of Artificial Intelligence (AI) in addressing the persistent challenges of blockchain technology across protocols, smart contracts, and distributed ledger management. Although blockchain offers decentralization, immutability, and transparency, its broader adoption remains constrained by scalability limitations, security vulnerabilities, inefficient consensus mechanisms, and the complexity of contract design and auditing. The findings of this review demonstrate that AI provides promising solutions to these barriers. Reinforcement learning (RL) applied to Proof-of-Stake reduced consensus latency by 30-50%, while NLP-based smart contracts lowered vulnerabilities by up to 40%, though both approaches introduced new concerns related to energy overheads and auditability. In addition, intelligent algorithms enhance ledger efficiency and data analytics, supporting more scalable and secure transaction processing. Drawing on 28 peer-reviewed studies published between 2018 and 2024, and guided by the PRISMA 2020 framework, this paper synthesizes state-of-the-art research, maps sector-specific applications in finance, healthcare, and supply chain management, and highlights unresolved gaps in ethics, reproducibility, and regulatory compliance. Notably, only 12% of the reviewed studies validated their approaches on live networks underscoring the gap between simulation-driven research and real-world deployment. The discussion culminates in the AIâBlockchain Interaction Model (AIBIM), a conceptual framework that systematizes synergies across consensus, contract, and application layers. By integrating empirical insights with critical evaluation, this work emphasizes the interdisciplinary nature of AIâblockchain research and provides actionable directions for advancing decentralized, scalable, and ethically aligned systems. This synthesis provides actionable insights for developers, regulators, and researchers in deploying AI-blockchain systems across finance, healthcare, and supply chains.