Today's distributed payment systems must function correctly despite the inherent presence of asynchrony, partial failures, and third-party integrations. Unlike typical RPC-based workflows used in software development, payment flows are heavily influenced by external delays, retries, timeouts, and nondeterministic state changes across multiple systems of record. A fault-tolerant ledger abstraction that decouples payment intent from execution enables safe retries and supports service events that may arrive out of order. Correctness and safety depend on distributed transaction constructs such as outbox/inbox patterns, compensation workflows, and time-bounded state machines to contain the effects of race conditions, double submissions, and ambiguous or indeterminate outcomes. A declarative reconciliation framework continuously verifies consistency between internal and external systems, enabling real-time anomaly detection and facilitating orchestration and recovery. These pragmatic engineering approaches, validated through simulations and production-level benchmarks, offer guidance for building resilient payment infrastructures in naturally asynchronous and failure-prone environments.
B. Thanikaivel, Vishnupriyan. J, Dharma Sastha B., Naveen Akash. V
This paper presents a Smart Contract Based Integrity Verifier framework for web security in APIs. This framework utilizes blockchain technology in order to design a secure mechanism that makes use of cryptographic hashes to verify the credibility of API responses. Smart contracts ensure transparent and tamper-proof scoring while automating security enforcement The approach is relevant because it covers the weaknesses existing in the existing Oauth 2.0 APIs and provides a strong mechanism for data tampering detection and prevention. This is achieved through the implementation of the system on Python Framework and blockchain simulation, hence enabling users to interact with the system and send API responses validated for credibility. The framework ensures$\mathbf{9 8 \%}$verification accuracy rates as compared to the existing security mechanisms for API Credibility Verification. Future work is in integrating into various domains, such as healthcare, finance, supply chain, and IoT.
Joel Poncha Lemayian, Ghyslain Gagnon, Kaiwen Zhang, Pascal Giard
Ethereum leverages smart contracts (SCs) to power decentralized applications (dApps), with execution handled by the Ethereum virtual machine (EVM) within an Ethereum client. Other blockchain platforms, including Avalanche, Polkadot, Aurora, and Cardano, have also adopted the EVM. However, the performance of the EVM is often constrained by the limitations of general-purpose processors, a challenge that has been explored in the literature. This work aims to further address the limitation by proposing EVMx, a dedicated single-core SC execution engine implemented on a field programmable gate array (FPGA). EVMx follows a processor-like architecture inspired by the RISC philosophy. By exploiting the parallelism and high-speed processing capabilities of FPGA hardware, EVMx achieves a 61% to 99% reduction in execution time for commonly used operation codes compared to traditional central processing unit (CPU)-based environments. Furthermore, EVMx executes entire Ethereum blocks with a percentage reduction in execution time between 6% and 56% against comparable FPGA implementations and 98% to 99% compared to CPU-based EVMs in the literature. These results demonstrate the potential of EVMx to significantly accelerate SC execution and enhance the performance of EVM-compatible blockchains.
A presente dissertação propõe o desenvolvimento de uma plataforma designada SoundSlice, que visa automatizar a gestão de direitos de autor em conteúdos musicais reutilizados e na criação de mixes, através da integração de tecnologias blockchain e contratos inteligentes. O sistema permite o registo de obras originais, reutilizações parciais, a combinação de múltiplas faixas em novas composições (mixes) e a atribuição automática de compensações aos titulares de direitos, assegurando transparência e rastreabilidade em todo o processo. A solução combina uma infraestrutura centralizada, suportada por uma base de dados MongoDB e armazenamento de ficheiros GridFS, com uma camada descentralizada baseada em Ethereum, responsável pela execução dos contratos inteligentes que formalizam a partilha de royalties. A nível prático, foi implementado um frontend web que permite o upload, análise, reutilização e criação de mixes musicais, bem como um backend Node.js que gere a lógica de negócio e a comunicação com a blockchain. O desenvolvimento da plataforma baseou-se nos conceitos teóricos e modelos de integração propostos pelos padrões Smart Contracts for Media (SC4M) e Interactive Music Application Format (IMAF), os quais orientaram a estruturação de metadados, a modelação de contratos e o desenho da arquitetura da plataforma. Por fim, foram conduzidos testes funcionais, de desempenho e de usabilidade que demonstraram o correto funcionamento da plataforma, a eficiência na execução de transações e a aceitação positiva por parte dos utilizadores, validando a viabilidade e o contributo da abordagem proposta.
A blockchain-and-smart-contracts architecture, which is suggested in the present research, can make public administration become more transparent, efficient, and trustworthy. The old-fashioned integrated systems of administration have the disadvantage of tampering with data, slow auditing and lack of trust of the population. To achieve these problems we develop a multi-layered solution that consists of: a user-facing application layer; a governance-rule automation layer driven by smart contracts; a Proof-of-Authority consensus layer that is fast with permissioned agreement; and a storage layer that integrates off-chain storage with immutable distributed ledger. To support the performance assessment, we establish mathematical models that are used to validate transactions, latency, cost, and transparency. The framework runs on a decentralized Hyperledger Fabric network with permissioned validator nodes and is tested on a real-world data sample of approximately 8, 000 government procurement records. Our experiments demonstrate significant improvements over conventional ERP-based systems: 2.8x increase in throughput, 65 percent decrease in average latency, an increase in a transparency index by 38 percent, 22 percent better data integrity, as much as 35 percent better fraud-detection effectiveness, and reduced operating cost per transaction by 39 percent. Through the introduction of read-only nodes, tamper-proof record and real-time audits, the suggested infrastructure enhances accountability and exemplifies how the blockchain-enabled governance can make the processes of the public sector more rapid, more secure, and people-focused.
The crisis of research integrity triggered by academic misconduct, such as scientific fraud and paper retractions, has emerged as a critical issue demanding urgent resolution within the academic community. Blockchain (BC), with its core features of distributed ledger, peer-to-peer transmission, consensus mechanisms, timestamps, and smart contracts, offers novel technical solutions for research institutions seeking efficient models of research credit supervision. By incorporating the psychological factors of risk perception among decision-makers and the dynamic evolution of behavioral decision-making, and drawing on prospect theory, this study has constructed an evolutionary game model involving researchers, scientific research institutions, and governmental entities to examine BC-enabled research credit supervision. This model analyzes the key determinants influencing scientific research institutions’ adoption of blockchain regulation (BC regulation), elucidates the behavioral characteristics and boundary conditions of research integrity among researchers under this new regulatory paradigm, and reveals the dynamic evolutionary trajectory of collaborative supervision between governments and scientific research institutions. The findings indicate the following: (1) Compared to traditional regulation, the BC regulation demonstrates superior regulatory effectiveness at equivalent levels of researcher integrity and misconduct costs, as well as under identical settings for reputational loss and penalties. (2) In addition to cost considerations and government subsidies, factors such as loss aversion coefficient, risk preference coefficient, and privacy breach losses are critical in influencing research institutions’ decisions to implement BC regulation. (3) The evolution of blockchain-empowered regulatory models encompasses three distinct evolutionary patterns. This study provides a theoretical foundation and a simulation case to optimize regulatory strategy formulation and resource allocation, thereby enhancing the effectiveness of research credit supervision.
With the advancement of the information age, the widespread application of electronic evidence in fields such as justice and finance has brought new challenges. Although existing blockchain electronic evidence sharing schemes have immutability and transparency, they still have shortcomings in access control, data privacy protection, and efficiency. In addition, traditional attribute encryption strategies lack effective revocation mechanisms and cannot fully protect privacy when implementing fine-grained access control. Therefore, in order to address the above limitations, a blockchain electronic evidence sharing scheme based on an improved ciphertext policy attribute encryption combined with zero knowledge proof technology has been proposed. The research innovatively introduces revocable ciphertext strategy encryption, which addresses the security risks caused by decryption key leakage through revocation function, ensuring the secure storage and sharing of electronic evidence. Meanwhile, the study also improved the PBFT consensus algorithm to enhance its performance in handling large volumes of transactions. The results showed that the storage TPS of the research model reached 492, and the query TPS reached 655. The computational cost of improving the PBFT consensus algorithm is 1.94 × 10 4 , and the maximum computational cost of the electronic evidence access control model based on zero knowledge proof is 509. Compared with traditional blockchain based electronic evidence sharing methods, the improved method not only enhances storage and sharing efficiency, but also further strengthens privacy protection capabilities by combining zero knowledge proof technology. In summary, the research method effectively achieves secure sharing and privacy protection of electronic evidence on blockchain, providing support and reference for electronic evidence storage in fields such as justice and finance. However, there are still challenges in terms of scalability and data storage in the research, so algorithms can be optimized in the future to further improve the application scope of the system.
Yuanfeng Xie, Weiwei Jiang, Hanqing Luo, Jian Ping Gan
This study presents an innovative authentication scheme that integrates Physical Unclonable Functions (PUFs) and Zero-Knowledge Proofs (ZKP) to provide efficient and secure authentication for Internet of Things (IoT) devices. Traditional PUF-based protocols offer strong security but incur high resource costs and slow authentication. To address this, we propose a joint scheme. First, a unified architecture combining a PUF–True Random Number Generator (TRNG) is introduced. This architecture utilizes a feedback permutation obfuscation mechanism and an arbitration delay deviation with a metastable design from a ring oscillator, ensuring the PUF–TRNG system possesses both attack resistance and true random properties. The architecture provides synchronization for both PUF and TRNG in the protocol. Next, we integrate Schnorr’s ZKP with a PUF-based key encapsulation and reconstruction scheme to construct an end-to-end anonymous identity authentication protocol that does not require real-time participation of a trusted third party. The protocol requires only two handshakes, significantly reducing the number of protocol rounds compared to related protocols. Finally, the PUF–TRNG architecture has been implemented on the Xilinx XC7A100T development board. Experimental results show that the PUF circuit effectively resists various modeling attacks. Formal verification with ProVerif demonstrates confidentiality, mutual authentication, and robustness against mainstream attacks. The protocol reduces area overhead and computational time by 43.04% and 42.99%, respectively, compared to similar protocols.
Physical Unclonable Functions (PUFs) and Hardware Security
This research introduces a post-quantum crypto-infrastructure exploiting the CRYSTALS-Kyber lattice based key encapsulation and the CRYSTALS-Dilithium digital signature cryptography supporting the real time payment transactions against quantum computing based attacks. Besides, based on the open quantum safe library OpenQuantumSafe and TensorFlow Federated, a hybrid classical-quantum federated key management protocol for payment systems is built. With the system, transaction validation can be checked without decryption based on HOMORPHIC encryption using Microsoft SEAL toolkit with sub-200ms latency. For anomaly detection application in neural network, LSTM auto-encoder can detect quantum based attacks with an accuracy of 97.4%. Implementation on AWS Braket Simulator for Verification against Shor algorithm Quantum Resistance and Grover algorithm. The model delivered Redis heavily boronated, for high speed key caching, and Apache Kafka, for asynchronous streaming of transactions at a speed of 50,000 transactions per second. Securely storing the cryptographic keys using an integration with HSM PKCS#11 hardware interface Blockchain anchoring with Hyperledger Fabric: GDPR compliance audit trail immutability with zero-knowledge-proofs (libsnark).
Ethereum is one of the key platforms in public blockchain networks. Ethereum used for various critical applications like decentralized finance (DeFi), smart contract based and supply chain operations etc., Because of its growing popularity along with its open and anonymous nature, network can contribute fraudulent or abnormal transactions. The existing anomaly detection methods suffer from real time detection as they use rule based or statistical methods. These methods are failed to identify dynamic or structural transactional anomalies and has no consensus verification. To address these challenges, in this study we designed an anomaly detection model that features rule based logic, Graph Neural Networks with GraphSAGE and Consensus-driven Complex Event Processing (CEP). We developed this model by combining three key procedures primarily the transactions are fetched from live Ethereum network and presented to rule based filtering. Graph construction and feature extraction will be done at second stage in the model to understand structural pattern and detects dynamically evolving abnormal patterns. In final phase consensus verification introduced as novel contribution in this model to reduce false positives. The framework tested for live Ethereum data that shows improvements detection accuracy of GNN model with low latency time. On the whole, the proposed system model supports practical security monitoring in Ethereum networks. This framework efficiently helps in detecting fraud or abnormal transactions that degrades the performance, reliability or accuracy of the decentralized network.
Internet of Things (IoT) facilitates intelligent interconnection and data exchange between devices. However, existing data aggregation schemes face challenges such as edge node disconnections, high computational overhead, and limited fault tolerance, which affect the reliability and efficiency of the system. To address these issues, this paper proposes a smart contract assisted and fault-tolerant data aggregation scheme without a trusted authority, named Cap. To realize it, we integrate blockchain technology and homomorphic encryption. Through smart contracts, we dynamically select and switch edge nodes to ensure that the data aggregation process continues even if some nodes go offline. Moreover, by leveraging homomorphic secret sharing, we effectively minimize communication overhead and ensure that users can exit the aggregation process without jeopardizing data integrity. This scheme provides an efficient and reliable solution for secure data aggregation in Iot environments and enhances the robustness and data security of the system. Performance evaluation show that, compared with existing schemes, Cap reduces computational and communication overhead by 30%.
Abstract While the volatile and unregulated cryptocurrency market is growing rapidly, little is known about what drives individual investor motivation to participate. This study investigates how trust, a proven predictor for stock market participation, is linked to cryptocurrency participation among 1,519 individual investors in Denmark, Finland and Sweden, countries characterised by high levels of digital adoption, trust and stock market participation. Our results show that individuals who trust strangers in relation to financial matters are more prevalent cryptocurrency participants, both in terms of current holdings and intended future holdings, compared to less trusting individuals. Furthermore, trust reduces how risky individuals consider cryptocurrencies to be and cryptocurrency knowledge raises people’s risk tolerance. Both trust and knowledge, therefore, contribute to increased cryptocurrency participation. Our study contributes to the debate about the mitigating role of trust for household investment decision making, extending its scope to the novel cryptocurrency market. This research is relevant for actors in the cryptocurrency market including developers, service providers, investors, and financial market regulators.
Traditional health insurance models depend on fixed policy structures that often lack personalization and transparency. These static systems are slow to respond to individual health behaviors, which leads to inefficient premium calculations and rigid coverage terms. In this paper, we propose a decentralized and smart framework that allows real-time negotiation of health insurance coverage using dynamic non-fungible tokens (dNFTs) and AI-powered risk profiling. Our system uses Ethereum smart contracts to represent health insurance policies as dNFTs that change based on individual health metrics collected from wearable devices or electronic health records. A builtin machine learning engine evaluates user risk profiles in realtime, updating the dNFT metadata as needed. Insurers interact with the system through a decentralized marketplace, where they bid to provide personalized coverage terms based on the live health profile encoded in the token. The negotiation process is trustless, transparent, and automatic using smart contracts, which removes intermediaries and cuts down processing time. We show a working prototype deployed on the Ethereum testnet and assess it using simulated user data. The results indicate a noticeable improvement in policy adaptability, personalization, and claim settlement time. This approach has the potential to transform health insurance by making it dynamic, data-driven, and fully decentralized.
Machine Learning in Healthcare
Artificial Intelligence in Healthcare and Education
C Ratna Prabha, K Mahesh Babu, Kattinti Dheeraj, N Varsha Priya · 5 authors
"Non-Fungible Tokens" (NFTs) have revolutionized digital ownership by enabling verifiable, unique assets on the blockchain. In this paper, we present an advanced, secure, and scalable "NFT marketplace" built on Ethereum, leveraging Zero-Knowledge Proofs (ZKPs), multi-signature wallets, and AI-driven threat detection to enhance security and decentralization. We implement ERC-721 and ERC-1155 standards, integrate Layer 2 solutions (Polygon zkEVM, Arbitrum) for cost efficiency, and introduce decentralized identity (DID) verification to prevent fraud. Our smart contracts were thoroughly tested using Slither, MythX, and manual audits, while the frontend employs React.js with WalletConnect v2 for cross-wallet compatibility. We also propose a novel royalty enforcement mechanism using ERC2981 with automated payouts via Chainlink Automation. Performance benchmarks demonstrate sub-second transaction finality on Layer 2 and gas cost reductions of up to 90% compared to traditional Ethereum mainnet deployments.
Magugliani, Fabrizio, Hoppe, Hans-Christian, Nortamo, Henrik
This is a white paper released as part of the ETP4HPC’s Strategic Research Agenda 6. A Federated Computing Infrastructure/Framework is the concrete implementation and operation term for a suite of technologies and functionalities aiming at the coordinated management and interoperability of data, resources, and processes across different systems, locations, and organizational boundaries. The framework envisions a decentralized architecture for creating and managing an interconnected network of resources where each participant (which could be a data centre, a system, an application, or any other device) may be used to achieve the intended result, breaking away from the traditional monolithic approach. This framework allows organizations to collaborate and share resources without fully giving up local control and thus to maintain a selectable level of autonomy. Federated Computing Infrastructures are designed from the beginning to be modular and adaptable, allowing resource-providing organizations to choose their level of involvement in the federation to ensure seamless integration with their existing platforms and regulatory requirements. The broader applicability of Federated Computing Infrastructure is pivotal for utilizing distributed data assets among and across organizations and within an organization. Federated Computing Infrastructures enable organizations and users to leverage distributed assets and drive innovation, collaboration, and value creation. The key characteristics of a Federated Computing Infrastructure/Framework are: 1. Decentralization: Each organisation participating in the Federated Computing Infrastructure makes available its resources while retaining a selectable level of autonomy and independence, 2. Interoperability: The Federated Computing Infrastructure is designed to enable the interaction among different systems built on different technologies, protocols, or standards. 3. Openness: The Federated Computing Infrastructure is open and accessible by any entitled organisation aiming at using its resources, provided that the authorization and data security criteria are met. 4. Scalability: The Federated Computing Infrastructure enables seamless scaling because new organizations, devices and resource-providing institutions can be added to the federation without interfering with the existing Infrastructure. 5. Data security and sovereignty: The concerns of data protection, data security and privacy-preserving computation are ubiquitous, and the Federated Computing Infrastructures provide tools and safeguards 6. Fault tolerance, redundancy and resiliency: Because resources are distributed across systems, locations, and organizational, the Federated Computing Infrastructure offers natural redundancy, enhancing the overall resiliency of the architecture. When one unit experiences a fault or is withdrawn from the pool of available resources, the problem is generally isolated to that particular unit, minimizing the impact on the entire system. The concept of Federated Computing Infrastructure/Framework provides a strategic framework that allows for the coordinated management and interoperability of data, resources, and processes. It’s particularly valuable in the EU environment for the exploitation of the computational resources deployed e.g. within the EuroHPC JU programs and initiatives, the Worldwide LHC Computing Grid (WLCG) and the likes, where it is of paramount importance for systems to be autonomous yet able to share compute power, data and resources effectively. However, the benefits come with their own set of challenges, such as increased complexity, inter-system security concerns and potential governance conflicts. Nonetheless, when designed and managed within a proper governance, adequate operational and policy guidelines, Federated Computing Infrastructures provide a powerful way to build flexible, scalable, and collaborative systems. This white paper explores the conceptual models of Federated Computing Infrastructure/Framework, the current deployments, the challenges facing the widespread exploitation of the Federated Computing Infrastructure and proposes a number of key R&I recommendations for a smooth and effective exploitation of the current and future deployments. Looking forward, the white paper outlines a Post Exascale Vision providing guidelines for designing future-proof Federated Computing Infrastructures.
The ethical tension surrounding AI-generated art often arises from misconceptions that anthropomorphize the algorithmic process. The accusation that “AI steals human creativity” overlooks the mediating role of human design and data literacy. This paper reframes the debate as a problem of informational asymmetry rather than morality. It proposes that Non-Fungible Tokens (NFTs) and Digital Object Identifiers (DOIs) can visualize and authenticate the flow of creative tension within a transparent ecosystem. NFTs serve as formal anchors—recording authorship, signature, and temporal origin—while DOIs preserve the conceptual framework and creative process. When linked, these two systems transform authorship into a traceable circulation of knowledge, allowing the boundary between plagiarism, homage, and originality to be objectively determined. This dual-layer provenance model presents an ethical infrastructure for creation in the age of generative AI.
Open access
2 source records
Scientific Computing and Data Management
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
Ibsen G. Bazie, Alidor M. Mbayandjambe, Kevin nguemdjom, Alain M. Kuyunsa · 9 authors
Africa’s agricultural sector employs over 60% of the continent’s population but faces challenges in product traceability and food security due to limited infrastructure and information asymmetries. This paper presents a blockchain-based traceability framework specifically designed for African agricultural supply chains, integrating smart contracts with electronic labelling (E-labelling) technologies to address socioeconomic constraints. We developed a decentralized system using Ethereum blockchain platform, implemented through Solidity smart contracts and a NextJS web application, optimized for low-bandwidth environments common in sub-Saharan Africa. The framework incorporates automated QR code generation enabling smallholder farmers to participate in transparent supply chains without extensive technical expertise. A comprehensive analysis of 13 blockchain applications revealed gaps in addressing African-specific challenges such as limited connectivity, multilingual requirements, and diverse regulatory environments. The proposed system is evaluated using the ADJENDE agribusiness case study design parameters from Burkina Faso, demonstrating practical applicability in West African contexts. Experimental validation shows functional product traceability with average transaction processing times of 20-25 seconds and gas costs of approximately 6,000,000 units per transaction. The prototype demonstrates capability for farmers to register crop information, track processing stages, and provide consumers with verifiable product authenticity through QR code scanning. This work presents a blockchain framework prototype designed for African agricultural contexts, addressing critical challenges of food security, rural economic empowerment, and consumer protection across the continent.
Shiva Johri, Amit Verma, Megha Sharma, N. Deepalakshmi
Cloud-based centralized credit scoring is accurate but suffers from network delays, high bandwidth costs, privacy concerns, and non-transparent decision-making processes. We present an edge intelligent finance framework that combines shallow neural models with symbolic, rule-based reasoning to make real-time, transparent loan decisions in the wild over low-cost edge devices. Trained via federated learning on popular benchmarks (German Credit, LendingClub, FICO), the framework achieves competitive discrimination performance (AUC ≈ 0.83) while reducing inference latency to 50ms and energy use to 0.3J/inference compared to their centralized deep models. An interpretable validation layer is proposed to encode fairness constraints and regulation rules, which outputs the interpretable rationales and group-gap metrics (<0.05). Under bandwidth constraints, stress tests indicate that accuracy and response times remain stable, as inference is performed locally. Our results demonstrate that under edge AI, efficient lending workflows and peer-to-peer ecosystems can be enhanced with privacy, fairness, and compliance through a series of information yellow-red-green cycles. We also address scalability, rule complexity, and deployment recommendations for financial institutions and regulators.
List decoding of codes can be seen as the generalization of unique decoding of codes while list decoding over finite fields has been extensively studied, extending these results to more general algebraic structures such as Galois rings remains an important challenge. Due to recent progress in zero knowledge systems, there is a growing demand to investigate the proximity gap of codes over Galois rings in Yizhou Yao(2025). The proximity gap is closely related to the decoding capability of codes. It was shown in Eli Ben-Sasson(2020) that the proximity gap for RS codes over finite field can be improved to $1-\sqrt{r}$ if one consider list decoding instead of unique decoding. However, we know very little about RS codes over Galois ring which might hinder the development of zero knowledge proof system for ring-based arithmetic circuit. In this work, we first extend the list decoding procedure of Guruswami and Sudan to Reed-Solomon codes over Galois rings, which shows that RS codes with rate $r$ can be list decoded up to radius $1-\sqrt{r}$. Then, we investigate the list decoding of folded Reed-Solomon codes over Galois rings. We show that the list decoding radius of folded Reed-Solomon codes can reach the Singlton bound as its counterpart over finite field. We also extend the deterministic pruning method of Vikrant Ashvinkumar(2026) to Galois rings, showing how to prune the affine free module obtained from the linear-algebraic decoder and recover the candidate codewords. Finally, we improve the list size of our folded Reed-Solomon code to $O(1/\varepsilon^2)$ by extending recent work in Shashank Srivastava(2025) to Galois Rings. By developing the recent work of Yeyuan Chen(2025), we show that folded Reed-Solomon codes over Galois rings satisfy the relaxed generalized Singleton bound in the average-radius sense with optimal list size $O(1/\varepsilon)$.
Mansi Gawade, Mayuri Hande, Vishakha Kshirsagar, Prof. S. Y. Mandlik
In This paper examines how blockchain technology and Non-Fungible Tokens (NFTs) can benefit the business landscape. NFTs are unique digital assets that represent real-world items and can be traded online using crypto currencies. Unlike fungible tokens, each NFT has a distinct digital signature, making them non- interchangeable. This system empowers artists and content creators to receive payment for their work without the need for traditional galleries. Moreover, NFTs can include a royalty feature, allowing creators to earn a percentage each time their NFT is sold again. Although still a relatively new concept, blockchain has the potential to transform the art and content creation industries by enabling the minting and trading of NFTs. The paper proposes that NFT marketplaces could serve as a central hub for various applications of NFTs.