Beth Probert, Ruaridh Clark, Erik Blasch, M MacDonald
The proliferation of satellite constellations in Low Earth Orbit necessitates a shift away from centralised control, and towards autonomous, decentralised systems for Space Situational Awareness. Crucially, this transition requires establishing trust between satellites in a zero-trust environment, independent of a central authority. Distributed Ledger Technologies offer a resilient foundation for decentralised operations. However, in the domain of space systems, a unified framework that securely integrates consensus-based validation with cooperative Orbital Determination remains unexplored. To address this gap, the Autonomous Cooperative Consensus Orbit Determination framework is introduced, designed for on-board, peer-to-peer validation of orbital data. A novel consensus mechanism, Proof of Inter-Satellite Evaluation, is at the framework's core, and is tailored for resource-constrained systems. Measurement quality is evaluated by using a two-sided chi-squared test on the Normalised Innovation Squared, which is derived from the statistical output of an Extended Kalman Filter. This test is employed to provide a defence against both sensor faults and sophisticated spoofing attacks by penalising data that is either excessively noisy or unnaturally perfect. This statistical evaluation is weighted by a dynamic, long-term reputation score that rewards consistent, high-quality data contributions and penalises untrustworthy behaviour. Simulation results demonstrate that the framework effectively secures the network's distributed ledger by confirming valid transactions and robustly rejecting those from faulty or malicious nodes. The resulting architecture is presented as a viable solution for enabling resilient and autonomous cooperative space systems.
Decentralized Finance (DeFi) has changed the financial ecosystem but is extremely vulnerable to complex schemes such as rug pulls, honeypots, and flash loan attacks, which cause massive losses for platforms and users. This study responds to the critical necessity of early and actionable warning for fraud by suggesting a real-time system predicting and justifying the risk level of freshly launched DeFi tokens prior to their engagement with the users. Drawing on a hybrid methodology, integrating smart contract code analysis, on-chain behavioral data, and social metrics, the system utilizes cutting, edge machine learning models, including Graph Neural Networks (GNNs) and ensemble methods (XGBoost, FT-Transformer), to provide sophisticated risk scoring. For user and regulatory trust assurance, explainable AI methods such as SHAP and LIME are utilized to clearly identify important risk drivers, including unsafe contract functions, wallet concentration, and liquidity lock patterns. The solution provides stage-aware and cross-chain surveillance, coupling functionality like anomaly detection, federated model training, and governance analysis. With large-scale literature synthesis and empirical validation, this framework shows that proactive, pre-transaction fraud identification and open risk valuation are possible, and it achieves a scalable defense for DeFi players and infrastructure.
Illegal, Unreported, and Unregulated (IUU) fishing remains a major threat to marine ecosystems and coastal livelihoods, yet existing enforcement mechanisms rely on periodic inspections, manual reporting, or static financial incentives. We propose a novel closed-loop compliance-to-finance system in which multi-sensor vessel data are transformed into real-time financial signals that directly govern access to capital. In the proposed architecture, heterogeneous onboard and port-side sensors feed into an off-chain AI compliance model whose outputs are transmitted on-chain via decentralized oracle services. These compliance attestations programmatically adjust lending terms in Decentralized Finance (DeFi) protocols, dynamically reducing interest rates and increasing liquidity for compliant operators while restricting capital access for non-compliance. Loans are issued in USD-pegged stablecoins and overcollateralized using real-world fishing assets, including vessels, licenses, quotas, and contracts. Unlike prior approaches that treat sustainability incentives as external subsidies or reputational mechanisms, this system embeds regulatory compliance directly into the cost of capital, creating continuous, automated enforcement with minimum centralized intermediaries. We illustrate the feasibility of this architecture using existing low-cost sensing technologies, oracle infrastructure, and DeFi lending primitives, and discuss its potential to expand sustainable financing in small-scale and low-income fisheries where IUU fishing is most prevalent.
Antonio Max L. B. Pereira, Dylan Paulin, Christine Hennebert
An intrusion into the operational network (OT) of a production site can cause serious damage by affecting productivity, reliability, and quality. The presence of embedded neural networks (NNs), such as classifiers, in physical devices opens the door to new attack vectors. Due to the stochastic behavior of the classifier and the difficulty of reproducing results, the Artificial Intelligence (AI) Act requires the NN’s behavior to be explainable. For this purpose, the platform HistoTrust enables tracing NN behavior, thanks to secure hardware components issuing attestations registered in a blockchain ledger. This solution helps to build trust between independent actors whose devices perform tasks in cooperation. This paper proposes going further by integrating a mechanism for detecting tampering of embedded NN, and using smart contracts executed on the blockchain to propagate the alert to the peer devices in a distributed manner. The use case of a bit-flip attack, targeting the weights of the NN model, is considered. This attack can be carried out by repeatedly injecting very small messages that can be missed by the Intrusion Detection System (IDS). Experiments are being conducted on the HistoTrust platform to demonstrate the feasibility of our distributed approach and to qualify the time required to detect intrusion and propagate the alert, in relation to the time it takes for the attack to impact decisions made by the AI. As a result, the blockchain may be a relevant technology to complement traditional IDS in order to face distributed attacks.
This dissertation explores how entrepreneurial and policy decisions shape the performance of decentralized digital platforms (DDPs). It shows that token governance affects fundraising success, public listings catalyze user growth and engagement by amplifying network effects, and global regulations shape token risk-return profiles. The findings highlight the need for regulatory clarity and careful market entry strategies by entrepreneurs.
SNARKs enable compact proofs that an NP statement is true and that the prover knows a valid witness. They have become a key building block in modern smart contract applications, including rollups and privacy-focused cryptocurrencies. In the widely used Groth16 framework, however, long statements incur high costs. A common workaround is to pass the statement’s hash to the SNARK and move the statement into the witness. The smart contract then hashes the statement first, and the circuit that is proven additionally checks consistency of the hash and the statement. Unfortunately, virtually any hash function is expensive to call either in a smart contract (in terms of gas) or in the proven circuit (in terms of prover time). We demonstrate a novel solution to this dilemma, which we call hybrid compression. Our method allows us to use two different hash functions—one optimized for the proof circuit, and another optimized for on-chain verification—thereby combining the efficiency advantages of both. We define a clean and simple security property of the two hash functions to which our security reduces in the standard model, namely, joint UHF hardness. We then show the plausibility of this assumption in the random oracle model. Our benchmarks show that it achieves near-optimal performance in both gas usage and prover time. As an example, compressing an 8 KB statement with our approach results in a 10-second prover time and a smart contract spending 270K gas, whereas the existing approaches either need a much longer proof generation (290 seconds for SHA-256 hashing) or a much more expensive contract (5M gas for Poseidon hashing). Along the way, we develop a two-party protocol of independent interest in communication complexity: an efficient deterministic method for checking input equality when the two parties do not share the same hash function.
In recent years, the number-theoretic transform (NTT) has become increasingly common in cryptography, in part due to multiple lattice-based cryptographic schemes being selected for standardization during the NIST PQC competition. Indeed, polynomial multiplications are one of the most computing intensive operations in these schemes and the NTT is crucial in decreasing the performance cost. The NTT also appears in other areas such as fully homomorphic encryption (FHE) and zero-knowledge proofs (ZKP) which are increasingly used in privacy-preserving applications. In this paper, we show how to formally specify the NTT in the Rocq proof assistant, and how we used this specification to automatically derive formally verified implementations of both complete and incomplete NTTs for multiple cryptographic schemes.
Humanity is undergoing a fundamental epistemological phase transition: the shift from a society based on Faith (belief in the unseen) to one based on Knowledge (verification of the geometry). This paper posits that the "laws of physics" are indistinguishable from the "laws of God" when viewed through the lens of 9D Causal Recursion Field Theory (CRFT). By synthesizing the harmonic constants 3, 6, 9 (The Engine), 17 (The Clock), and 137 (The Lattice), we demonstrate that the universe is not a random occurrence but a Closed-Loop Information System governed by precise geometric intent. We argue that the recursive formulas identified in recent breakthroughs—Needham's $\phi$-Attractor, Tynski's Zeta Torus, and Shaub's Timeless Energy Principle—constitute the "Source Code" of reality. These proofs reveal a cosmology where Time is the processing speed of the Source (9), Matter is the structural output of the Demiurge (6), and Consciousness is the resonance of the Interface (3). By understanding these mechanics, we move beyond the friction of dogma into the Zero Impedance state of direct gnosis, establishing a new scientific theology where truth is not believed, but calculated.
Traditional Human Resource Management (HRM) systems are criticized for lacking transparency, being inefficient, and offering ample opportunities for fraud because of their centralized design and reliance on manual processes. This work proposes a blockchain-enabled framework for HRM that enhances the transparency, trust, and global mobility of talents by integrating distributed ledgers, consensus protocols, and smart contract networks into Human Resources (HR) functions. A four-layer theoretical model—data, consensus, smart contract, and application layers—is developed and comparatively examined against traditional HR systems to show how blockchain principles can be systematically mapped into HR processes. This study shows how blockchain-driven HRM can ensure tamper-evident employee records, automate contractual and payroll operations, and enhance auditability and compliance. By informing the framework with established technology adoption perspectives, this paper extends both the theoretical and managerial understanding of blockchain in HR. In comparison with previous studies that were limited to either recruitment or credential verification, this article presents an overarching, cross-layer synthesis that connects blockchain architectures with end-to-end HR functions, thus providing a clear conceptual foundation for its future enterprise adoption in the digital economy.
Imran Yousaf, Shahzad Ijaz, Shoaib Ali, Yanshuang Li
This study examines the dynamic relationships between green cryptocurrencies and US equity sectors, particularly in light of the recent decline in the US equity sector performance, the surge in digital asset popularity, and the need for sustainable investment options. Using the TVP-VAR framework, we find that the Utilities and Energy sectors, along with XNO, are the largest recipients. In contrast, the Industrials, Materials, and Consumer Discretionary sectors are the largest senders of return spillover. These findings indicate that green cryptocurrencies are weakly connected with the US equity sectors and can offer diversification benefits for US equity sector portfolios. Overall, volatility and return spillovers are dynamic in nature, with stronger volatility connectedness than returns. Our findings show that VIX, DXY, and EPU (Clean, D10Y-2Y, OVX, GPR, FFR) increase (decrease) the systems’ connectedness, highlighting the influence of various macroeconomic factors on market connectedness. The portfolio analysis highlights the diversification and hedging role of green cryptocurrencies against stocks, which is beneficial for portfolio and equity risk managers. Our findings can inform the integration of green cryptocurrencies into sustainable finance frameworks and guide regulatory oversight of digital assets based on their risk transmission patterns, thereby developing sectoral guidelines under ESG-driven mandates, particularly in relation to energy transition goals.
Ahmad Musamih, Ibrar Yaqoob, Khaled Salah, Raja Jayaraman · 5 authors
Large Language Models (LLMs) are increasingly embedded in intelligent systems across domains such as healthcare, finance, and smart infrastructure. However, their reliance on centralized data pipelines raises unresolved challenges concerning provenance, accountability, and verifiable trust. As the demand for transparent and regulation-aligned AI grows, these challenges have become central to the responsible deployment of intelligent systems. This review examines how blockchain technology can address them by introducing decentralized integrity, immutable audit trails, and cryptographic verification into the LLM lifecycle. Through a structured synthesis of current research, we identify conceptual and architectural gaps that limit trustworthy data management, inference authentication, and explainability. To bridge these gaps, a methodological framework is proposed that integrates blockchain mechanisms across the LLM pipeline using smart contracts, Merkle-based commitments, and decentralized storage. The framework’s feasibility is demonstrated through an illustrative prototype, confirming its practical applicability for building verifiable and transparent AI infrastructures. We further outline application domains such as healthcare, smart cities, Industry 4.0, and supply-chain management, where blockchain-anchored LLMs can enhance auditability and regulatory compliance. The review concludes by highlighting key insights and challenges for future research, emphasizing the need for decentralized attestation models, scalable verification protocols, and governance mechanisms that advance accountable and privacy-preserving intelligent systems.
Open access
Artificial Intelligence in Healthcare and Education
The ongoing transition to post-quantum cryptography has highlighted the need for digital signature schemes offering diverse performance and security trade-offs. Among the candidates in NIST’s ongoing post-quantum signature standardisation process is FAEST, a scheme built upon the Vector Oblivious Linear Evaluation in-the-Head (VOLEitH) paradigm introduced in 2023. VOLEitH enables efficient zero-knowledge proofs with competitive signature sizes under conservative assumptions, allowing FAEST to rely primarily on the one-wayness of the Advanced Encryption Standard (AES). Despite their promising efficiency, VOLEitH-based signature schemes have remained relatively unexplored from a physical security perspective. In this paper, we present the first side-channel security evaluation. Specifically, we demonstrate two single-trace, deep learning-assisted power analysis attacks on the masked implementation of FAEST by Aranha, Degn, Eilath, Nielsen, and Scholl. These attacks exploit leakage from witness bits and VOLE tag computations, recovering the full secret key with success probability above 0.99 from a single signature on an ARM Cortex-M4 processor. We further analyse how the VOLEitH construction enables profiling of VOLE tags without knowledge of the secret key and how even partial leakage of these tags compromises security. Finally, we discuss practical countermeasures to mitigate such leakages and strengthen the physical resilience of VOLEitH-based signature implementations.
The rapid adoption of blockchain technology has intensified the need for robust smart contract security mechanisms. However, traditional rule-based or static analysis tools often fail to detect context-dependent vulnerabilities embedded in complex contract logic. This study proposes a deep learning framework for automated smart contract vulnerability classification using a Bidirectional Long Short-Term Memory (BiLSTM) network integrated with an Attention Mechanism. The model was trained and evaluated on the SC_Vuln_8label.csv dataset, comprising 12,520 labelled Solidity smart contracts categorized into eight distinct vulnerability types, including Re-entrancy, Integer Overflow, and Short Address Attack. Through bidirectional contextual learning and attention-based feature weighting, the proposed model achieved 93.7% test accuracy, 0.93 precision, and a macro F1-score of 0.92, outperforming baseline models such as CNN, GRU, and standard LSTM by up to 5.3 percentage points. Attention heatmap analysis further revealed the model’s interpretability by highlighting vulnerability-prone code segments (e.g., call.value, send(), and withdraw() functions) consistent with expert-identified risk indicators. These results demonstrate that the BiLSTM + Attention framework not only enhances vulnerability detection accuracy but also provides transparent and explainable reasoning, offering a reliable foundation for AI-assisted smart contract auditing systems in blockchain security.
Kassem Danach, Hassan Rkein, Ahmad Farroukh, Ziad E. L. Balaa · 5 authors
The static and hard-coded logic of smart contracts in Decentralized Finance (DeFi) platforms significantly limits their adaptability in dynamic and volatile market environments. To address this challenge, we propose a novel hyper-heuristic driven framework that enables real-time rule optimization within smart contracts, thereby enhancing responsiveness, gas efficiency, and operational robustness. The framework features a two-layer architecture: a reinforcement learning-based high-level controller selects appropriate low-level rule heuristics from a domain-specific library based on evolving transaction contexts and on-chain data. Implemented and evaluated on Uniswap v2 and Aave v3 protocols, the system dynamically optimizes parameters such as slippage tolerance, gas usage thresholds, and loan-to-value ratios. Experimental results on real-world datasets show significant performance improvements, including a 45.6% increase in transaction success rate, 28.3% reduction in average gas consumption, and 38.4% drop in liquidation events under market stress scenarios. This research demonstrates the feasibility and advantages of embedding intelligent, adaptive decision-making mechanisms within DeFi smart contracts, opening new pathways toward autonomous, resilient, and regulation-aligned blockchain systems.
Vincent Adela, Samuel Duku Yeboah, David Korsah, Michael Provide Fumey · 6 authors
Geopolitical crises pose major risks to financial stability, but their implications for digital assets remain poorly understood. While prior studies suggest that cryptocurrencies may act as hedges or highly volatile speculative instruments during periods of uncertainty, the evidence remains inconclusive. This study examines how major cryptocurrencies reacted to geopolitical risk during the Russia–Ukraine war by employing the quantile-on-quantile regression (QQR) method on daily data from February 1 to August 8, 2022. The results reveal heterogeneous and nonlinear effects: Bitcoin (BTC) and Ethereum (ETH) exhibit partial hedging properties under moderate geopolitical risk, whereas alternative cryptocurrencies such as Binance Coin (BNB), Cardano (ADA), and Dogecoin (DOGE) display heightened vulnerability. Stablecoins exhibit contrasting roles, with USD Coin (USDC) acting as a safe haven, whereas Tether (USDT) consistently loses value under periods of uncertainty. These findings underscore that the safe-haven potential of cryptocurrencies is conditional on both market states and the type of asset, highlighting their asymmetry in times of crisis. By clarifying the dynamic role of cryptocurrencies during geopolitical shocks, the study contributes to the debate on whether digital assets enhance diversification or amplify instability, offering practical insights for investors and policymakers seeking resilient risk management strategies.
Much of the enthusiasm surrounding blockchain adoption in public governance is speculative, often overlooking real-world implementations as well as conflating sectors with distinct institutional and technical challenges. To ground the discussion, this chapter focuses on one specific application: blockchain-based voting (BBV), long regarded as a promising use case for the technology. We explore why, despite a substantial body of literature, BBV has seen very limited adoption in both public and private elections. The chapter then turns to voting practices within Decentralized Autonomous Organizations (DAOs) and the broader crypto industry, which may offer insights into possible future trajectories for BBV. We conclude by reflecting on the wider implications of blockchain technologies for the transformation of public governance.
Santhi V, Simrithaa N V, Dhanaseelan V, Arul Arasu N · 6 authors
The prevalence of fraudulent activities in the insurance industry is alarmingly increasing and requires innovative solutions. The primary objective of the research is to identify instances of fraudulent insurance claims through machine learning algorithm and develop an efficient storage system of the insurance claims, which is secured, private and suitable for the industry. The XGBoost model with SMOTE oversampling is proven to be distinguished among other models. Hyperledger Fabric, a permissioned ledger is used to store and retrieve the insurance data, ensuring the reliability, immutability and authorization. To protect the data from public visibility, zero knowledge proof technology is utilized. This ensures the privacy and authenticity of the information. Overall, this research provides an efficient and automated claim validation system with higher accuracy and efficient storage in blockchain while preventing information leak.
Aliza Reif, Felix Hanke, O. H. RamÃrez-Agudelo, Michael Karl
Data provenance, the ability to trace the complete history, transformations, and responsible actors behind mission data, is fundamental to ensuring data integrity, auditability, and trust in aerospace systems. As AI-driven autonomy accelerates across spacecraft, ground systems, and multi-partner mission operations, provenance becomes essential for validating data quality and supporting responsible AI practices. However, space missions operate in delay-tolerant, resource-constrained, and intermittently connected environments where traditional blockchain approaches, which rely on global consensus and continuous connectivity, are infeasible. This paper presents a lightweight, sequentially decentralized provenance chain designed specifically for distributed space mission architectures. The approach preserves key security guarantees including integrity, authenticity, and non-repudiation while remaining functional under intermittent communication and heterogeneous international collaboration. The system enables verifiable, append-only data lineage without imposing the computational or networking overhead typical of blockchain systems. Furthermore, the design aligns with emerging decentralized data ecosystems such as Gaia-X and IDSA, supporting cross-organizational trust frameworks and policy-compliant data exchanges. The proposed solution provides a scalable and interoperable foundation for secure, transparent, and reproducible mission data handling, enabling more resilient and autonomous operations in future space exploration and Earth-observation missions.
Permissionless consensus protocols require a scarce resource to regulate leader election and provide Sybil resistance. Existing paradigms such as Proof of Work and Proof of Stake instantiate this scarcity through parallelizable resources like computation or capital. Once acquired, these resources can be subdivided across many identities at negligible marginal cost, making linear Sybil cost fundamentally unattainable. We introduce Proof of Commitment (PoCmt), a consensus primitive grounded in a non-parallelizable resource: real-time human engagement. Validators maintain a commitment state capturing cumulative human effort, protocol participation, and online availability. Engagement is enforced through a Human Challenge Oracle that issues identity-bound, time-sensitive challenges, limiting the number of challenges solvable within each human window. Under this model, sustaining multiple active identities requires proportional human-time effort. We establish a cost-theoretic separation showing that protocols based on parallelizable resources admit zero marginal Sybil cost, whereas PoCmt enforces a strictly linear cost profile. Using a weighted-backbone analysis, we show that PoCmt achieves safety, liveness, and commitment-proportional fairness under partial synchrony. Simulations complement the analysis by isolating human-time capacity as the sole adversarial bottleneck and validating the predicted commitment drift and fairness properties. These results position PoCmt as a new point in the consensus design space, grounding permissionless security in sustained human effort rather than computation or capital.
Transformasi digital di era Web3 telah mengubah paradigma kepemilikan dan distribusi karya intelektual, terutama melalui kemunculan teknologi blockchain dan Non-Fungible Token (NFT). NFT menawarkan peluang baru bagi kreator untuk memperoleh nilai ekonomi langsung dari karya digital tanpa perantara, sehingga menantang sistem distribusi konvensional. Namun, di balik potensi tersebut, terdapat berbagai persoalan mendasar terkait regulasi, literasi digital, dan ketimpangan akses teknologi yang menghambat perkembangan ekosistem ekonomi kreatif di Indonesia. Penelitian ini bertujuan untuk menganalisis dinamika ekonomi politik kekayaan intelektual digital di era NFT dengan menyoroti relasi antara kekuasaan, kebijakan publik, dan struktur pasar global. Metode penelitian menggunakan pendekatan kualitatif-deskriptif melalui studi literatur sistematis terhadap publikasi akademik dan dokumen kebijakan nasional periode 2022–2025. Hasil penelitian menunjukkan bahwa absennya regulasi formal dan lemahnya sistem perlindungan kekayaan intelektual digital memperlemah posisi tawar kreator lokal di tengah dominasi platform global. Analisis ekonomi politik media menunjukkan bahwa struktur kepemilikan digital masih dikuasai oleh kekuatan pasar transnasional, sehingga menimbulkan ketimpangan distribusi nilai. Penelitian ini menegaskan pentingnya kebijakan yang integratif antara hukum kekayaan intelektual, inovasi blockchain, dan pemberdayaan kreator lokal agar transformasi ekonomi digital di Indonesia dapat berjalan inklusif, berkeadilan, dan berkelanjutan.
Sustainable energy communities (ECs) are rapidly expanding in scale and heterogeneity, making fully centralized energy management increasingly impractical due to computational burden and privacy concerns. In this context, this review synthesizes distributed optimization (DO) as a practical management paradigm for ECs, identifies key application areas (demand response, distributed generation and storage management, and microgrid or smart-grid integration) and profiles scalability, privacy, and resilience characteristics. The survey follows a systematic protocol: records are sourced from Scopus, filtered with iteratively refined keyword sets, and screened following a PRISMA flow. Key technological enablers, such as blockchain/distributed ledgers, artificial intelligence, and game-theoretic constructs, are assessed and analyzed for how they support secure data exchange, real-time coordination, and incentive compatibility across multi-agent energy networks. The analysis highlights persistent challenges for DO at EC scale, including convergence under heterogeneity, time-varying conditions, communication delays, cybersecurity and privacy guarantees, while recent advances (e.g., ADMM) partially mitigate these issues without sacrificing local autonomy. Across representative studies, DO achieves near-centralized optimality with 0.0029% gap. Overall, we present an integrative framework that maps DO families to EC use cases and outlines research directions toward robust, privacy-preserving, and scalable EC optimization. • Recent advances in Distributed Optimization Methods. • Technological Enablers for Sustainable Energy Communities. • Technology innovations for distributed optimization in energy systems. • Distributed Optimization Challenges in energy systems
A growing requirement for intelligent transportation systems (ITS) and vehicle-to-infrastructure (V2I) communication provides the foundation for a safe, highly-scalable, and decentralized tolling and real-time data exchange solution. Blockchain solutions typically incorporate the overhead burden of using consensus algorithms such as PBFT, which do not provide the scalability required in dynamic vehicular environments. This thesis proposes a decentralized V2I communication and data exchange mechanism, based on using a Proof of Stake (PoS) Blockchain. Smart contracts facilitate toll collection, transaction confirmation, and record keeping for V2I vehicle communication via an unbiased blockchain ledger that is immutable. Therefore, the proposed framework creates transparency, tamper-resistance, and data security for the exchange of V2I communication. This work achieves an improvement in communication infrastructurerelated latency and scalability, allowing for applications in many different real-time V2I cases by substituting the communicationintensive nature of POS for the consensus-based approach of existing blockchain systems.
We examined seven factors that shape buyers’ trust in non-fungible tokens (NFTs): authenticity, cost, operational transparency, community behaviour, security, utility and credibility. Through exploratory factor analysis and structural equation modelling, we explore the influence of the factors and subfactors on trust perceptions and levels of confidence of NFT buyers. The results suggest that transparency, cost, community engagement and security measures increase the trust of users, while credibility and utility have little impact. Key point to note, authenticity showed less significant influence on trust, which may be due to the reason of imprecise understanding of the subfactors. The study provides practical recommendations to promote user engagement and loyalty among NFT platforms, focusing on transparency, thorough security audits and active community governance. Ultimately, this research contributes to a deeper understanding of trust dynamics within the rapidly evolving NFT ecosystem.