This article discusses the practical implementation of a prototype academic transcript storage system based on blockchain technology and smart contracts. The digital transformation of higher education requires reliable mechanisms for ensuring the integrity and verifiability of academic documents. It presents the design and experimental validation of a blockchain-based system for storing and verifying academic transcripts within the higher education system of the Republic of Kazakhstan. The proposed solution is based on an Ethereum Virtual Machine-compatible smart contract implemented in Solidity and deployed on a test network. The testnet was used as the experimental environment, and transaction monitoring was performed using the BlockScout v11.0.3 explorer. The architecture of the TranscriptStorage smart contract is presented, including a role-based access model, a data indexing mechanism using keccak-256, and storage of transcripts in a mapping structure (bytes32 => Transcript[ ]). The experimental results confirm the successful recording of the Transcript in the distributed ledger, event recording (Logs), and the correctness of the ABI encoding of input parameters (Raw Input), as well as a change in state (State Changes) reflecting the fee payment. The use of events is shown to enable cost-effective third-party data verification without the need to store the entire text in the contract state. The comparative results showed that the proposed system reduced gas consumption by 804.5% compared to Blockcerts, 48.8% compared to ECertChain, 82.5% compared to ShikkhaChain, and 43.5% compared to zkEVM. These improvements were achieved while maintaining high scalability, robust privacy features, and security, making it a practical solution for Kazakhstanâs educational system.
Eunchan Park, Kyonghwa Song, Won Hoi Kim, Wonho Song ¡ 5 authors
Traditional blockchain untraceability schemes, such as mixers and privacy coins, obscure the sender-receiver relationship by placing transfers within an anonymity set. This paper studies a stronger goal: whether the transfer event itself can be made unobservable by blending into common decentralized-finance (DeFi) activity. We introduce Deniable Covert Asset Transfer (DCAT), a class of transfers that stage common loss-producing events, such as sandwich and arbitrage operations, so that a sender appears to suffer an ordinary loss while the receiver appears to profit from it. We design and validate two DCAT instantiations: a sandwich-based transfer on Ethereum and an arbitrage-based transfer on Arbitrum. Our experiments show that, under the evaluated settings, DCAT transfers are empirically unobservable on both chains. They are syntactically identical to corresponding maximal extractable value (MEV) activities, classified as ordinary extractions by standard MEV detection tools, and leave the sender and receiver unlinked under representative forensic tools. Since syntactic inspection cannot distinguish DCAT from ordinary MEV activity, we examine whether economic semantics provide useful forensic signals. Through a large-scale study of MEV losses on Ethereum and Arbitrum, we show that key semantic features follow power laws. Extreme losses and repeatedly exploited addresses occur in the wild, and thus are not by themselves definitive evidence of collusion. This gives staged transfers plausible deniability and makes fixed-threshold detection prone to false positives. We therefore develop a multivariate statistical method for forensic triage that ranks incidents by the joint rarity of their economic footprint. Applied to real-world DeFi activity, our method narrows a large search space to suspicious cases for manual investigation; we present three such cases to illustrate this prioritization.
Smart cities require the efficient and secure integration of key infrastructure domains such as water, energy, transportation, smart lighting and waste management deploying a myriad of data-generating IoT sensors and devices. Current management systems feature single points of failure, lack of auditability, insufficient privacy protection and lack of scalability as IoT nodes increase in density. In this paper, we present SmartChain, a three-tier multilayered blockchain based architecture that incorporates a permissioned distributed ledger, an AIbased anomaly detection module (ADM) and a dual-layered privacy preservation approach that combines Zero-Knowledge Proofs (ZKP) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE). SmartChain is tested on a large dataset - 2000 timestamped transactions involving Smart Citiesâ five infrastructure types across several zones of a city. The results show a mean throughput of 3,421 transactions per second (TPS), a mean transaction latency of 3,847 milliseconds and a mean privacy score of 82.4 out of 100. The machine learning based anomaly module produces an F1-Score of over 97% and an AUROC score of 0.991 with Random Forest as the classifier. Benchmarking against Hyperledger Fabric 2.5, Ethereum 2.0 and the IOTA Tangle demonstrate the scalability, security, privacy and efficiency of SmartChain. This research renders SmartChain a practical production-level platform for management of new smart city infrastructure.
Vote Chain is a fully implemented, decentralized e-voting application (DApp) built on Ethereum. Existing blockchain-based voting systems often suffer from either high computational overhead due to homomorphic encryption or lack of fully deployable, adversarially tested implementations. To address these limitations, VoteChain employs a keccak256-based commitâreveal protocol to preserve ballot secrecy during the voting phase, with Solidity 0.8.20 smart contracts enforcing all election rules autonomously. Wallet-based authentication via MetaMask eliminates centralized identity management. The system is validated through 14 automated unit tests (all passing in 615 ms) covering correctness, access control, double-voting, hash forgery, and phase-bypass attacks. Per-voter gas cost is approximately 120,000 units (commit and reveal combined). An ablation study confirms the non-redundant contribution of each architectural component. Comparative analysis shows that VoteChain achieves vote privacy without homomorphic encryption while maintaining full decentralization and implementation completeness. The system is evaluated and validated on a local Hardhat network, with the architecture readily extensible to Layer-2 rollups for large-scale elections.
The Ethereum blockchain utilizes the EIP-1559 algorithm to manage transaction inclusion and block assembly. However, EIP-1559 and much of the existing literature study this problem from a static perspective, focusing on price evolution without modelling transaction dynamics within the mempool. Motivated by this limitation, we study a dynamic transaction scheduling problem in which transactions with heterogeneous sizes and per-unit values arrive over time and remain in the mempool until scheduled. To capture the stochastic mempool evolution, we formulate the problem as a Markov Decision Process (MDP) whose state represents the mempool configuration and whose actions correspond to block prices. We first provide a primal-dual interpretation of the static EIP-1559 mechanism, showing that block prices arise naturally as dual variables of a social-welfare maximization problem. Building on this perspective, we extend the framework to the dynamic setting and formulate an objective that maximizes long-run discounted reward while incorporating holding costs and overshoot penalties. We then employ a Natural Policy Gradient (NPG) algorithm to compute the optimal policy. Our results show that dynamic pricing stabilizes the mempool while maximizing long-run discounted reward. In particular, as the overshoot penalty increases, the average scheduled transaction volume converges to the target block capacity, and the resulting NPG updates closely resemble the EIP-1559 price update rule. Finally, we study two special cases of the MDP formulation: homogeneous transactions and uniform arrivals. In the homogeneous setting, where the protocol directly controls scheduled volume, we show that the optimal policy has a threshold structure. We then propose a bang-bang pricing mechanism for uniform arrivals and derive a lower bound on the block capacity needed to ensure system stability.
Abstract This study uses high-frequency price data to analyze risk connectivity among 15 cryptocurrencies, focusing on moments such as volatility, skewness, kurtosis, and jumps during the pre-COVID-19 era, the COVID-19 epidemic, and Russian-Ukrainian tensions. The results indicate that Ethereum Classic is a major shock transmitter in all periods, and this effect becomes more pronounced during geopolitical crises. In contrast, Stellar, Tezos, and Tron are important shock absorbers, particularly during market volatility. Jump risk analysis confirms the dominance of Ethereum Classic and its capacity to increase spillover risks during crises. For higher-order moments, the findings reveal that Bitcoin, Ethereum, and Dash are significant transmitters of skewness spreads, whereas Dash and Eos are significant transmitters of kurtosis spreads. Jump risk analysis confirms the dominance of Ethereum Classic and its capacity to increase spillover risks during crises. These findings highlight the need for targeted risk management strategies adjusted to cryptocurrency market dynamics.
Federated Learning (FL) enables collaborative model training while preserving data privacy but relies on centralized aggregation servers, leading to issues such as lack of transparency, vulnerability to malicious updates, and single points of failure. This paper proposes a decentralized federated learning framework integrating blockchain technology and the InterPlanetary File System (IPFS) to eliminate central authority and enhance trust. Smart contracts deployed on the Ethereum Sepolia testnet manage model submission, validation, incentive distribution, and reputation tracking. Model updates are stored off-chain using IPFS, while their hashes are recorded on the blockchain to ensure integrity and immutability. A staking and slashing mechanism is introduced to encourage honest participation, where valid contributions are rewarded and malicious updates are penalized. A reputation system further evaluates participant reliability over time. The system is implemented using PyTorch, Solidity, Web3.py, and React.js. Experimental results demonstrate improved security, transparency, and efficient decentralized coordination, highlighting the feasibility of integrating federated learning with blockchain and decentralized storage for scalable and trustworthy machine learning applications.
Autonomous AI agents are increasingly deployed on blockchain platforms, yet the design space that governs their interaction remains poorly understood. This convergence, where autonomous agents operate on and within decentralized systems, is a defining feature of the emerging Web~4.0 paradigm. This paper presents a Systematization of Knowledge organized around a bidirectional trust framework. In the B $\boldsymbol{\rightarrow}$ A direction, we examine how blockchain provides trust infrastructure for agents, spanning identity and account abstraction, permission and delegation, intent-centric execution, and tokenized agent economies. In the A $\boldsymbol{\rightarrow}$ B direction, we examine the reverse: how AI agents participate in core blockchain mechanisms including security auditing, consensus, and governance. A Trust Foundation of verifiable computation underpins both directions, with each primitive offering different trade-offs between trust minimality, computational overhead, and deployment readiness. We formalize the interaction as an Agent-Blockchain Interaction Model (ABIM), catalog 70 Ethereum EIPs/ERCs, examine 20 representative industry projects, and review 118 academic papers, applying a five-dimensional framework assessing Verifiability, Minimality of Trust, Expressiveness, Composability, and Maturity. Our analysis uncovers significant gaps: the agent-specific standards ecosystem is overwhelmingly immature, intent architectures lack formal analysis, and while isolated works have begun to explore AI participation in consensus and governance, a unified security framing that treats AI as a first-class actor at the protocol layer remains absent. We propose a three-dimensional taxonomy, identify nine concrete open problems, and highlight the sharpest research opportunities at this intersection.
Smart contract security has progressed from vulnerability detection toward a broader research agenda that includes semantic reasoning, automated repair, adversarial robustness, and real-time exploit detection. This paper develops a capstone-oriented research narrative around four directions: foundation-model-based smart contract semantics and vulnerability reasoning [1], automated smart contract repair with formal guarantees [2], adversarial learning for robust malicious contract and transaction detection [3], and real-time transaction-level exploit detection at blockchain scale [4]. We connect these directions to two recent studies that characterize the current frontier: a diagnostic analysis of where smart contract security analyzers fall short [5] and a scalable real-time system for malicious Ethereum transaction detection [6]. The resulting framework is intended to help students formulate capstone projects that are technically grounded, empirically measurable, and aligned with contemporary smart contract security research.
Active asset managers are increasingly including cryptocurrencies in their alternative asset allocations, highlighting their speculative and volatile nature. The aim of this research is to examine trends in the returns and volatility of cryptocurrencies while accounting for the depegging of stablecoins driven by speculative trading macroeconomic shocks, and technological shifts. It builds a sample, by market capitalisation, using data from the daily closing prices of Bitcoin (BTC), Ethereum (ETH), Binance (BNB), and Ripple (XRP), two fiat-backed stablecoins (USDT and USDC) and a cryptocurrency-collateralised stablecoin (DAI). As a first step, Granger causality tests were applied to examine the influence of stablecoin depegging events on crypto returns during financial market stress. The results indicate that DAI exhibits the most consistent Granger-causal relationship with cryptocurrency returns; whereas, the predictive power of USDT and USDC depegging events varies across assets. The analysis was extended by modelling volatility using an EGARCH-X model to study whether depegs also affect crypto during periods of market stress. In this case, the evidence for statistically significant effects is limited. Nevertheless, in the instances where significance is detected, the results are consistently linked to USDC.
This study aims to analyze the volatility dynamics and spillover phenomena among major crypto assets (Bitcoin, Solana, and Ethereum) and their relationship with the Jakarta Composite Index (JCI), a proxy for the Indonesian capital market. In the era of digital financial integration, the link between speculative crypto asset markets and conventional stock markets is a crucial issue for financial system stability. This study uses daily price time series data for the period 2020-2025. The analysis was conducted using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the Diebold-Yilmaz spillover index approach to measure the magnitude of shock transmission between markets. The results indicate significant volatility transmission among the three crypto assets, with Bitcoin remaining the primary source of volatility. Furthermore, this study finds an increasing dynamic correlation between the global crypto market and the Indonesian capital market during periods of economic uncertainty. These findings have important implications for investors in portfolio diversification strategies and for Indonesian regulators in monitoring systemic risks originating from digital assets.
Keir Finlow-Bates, Markus Jakobsson, Hossein Siadati
The transition to post-quantum cryptography in blockchain systems such as Bitcoin and Ethereum is often framed as a purely cryptographic problem. In practice, it also presents significant economic and infrastructural challenges: in globally replicated networks, increases in transaction size and verification cost are multiplied across all participating nodes. Existing post-quantum signature schemes, including lattice-based constructions such as CRYSTALS-Dilithium and stateless hash-based schemes such as SPHINCS+, introduce substantial increases in signature size. At blockchain scale, these increases translate into higher storage, bandwidth, and validation requirements, potentially requiring multiple generations of hardware improvement to become operationally routine. Historical experience suggests that even moderate increases in data footprint can be contentious, as illustrated by the Bitcoin block size debates (2015--2017). We propose a hash-based commit--reveal construction that replaces a single signature-bearing transaction with two lightweight transactions, each containing a fixed-size (32-byte) hash output derived from well-established primitives such as SHA-256, BLAKE, or Keccak. This approach achieves post-quantum security under standard hash assumptions while increasing the effective transaction footprint by only approximately 1.5$\times$ to 2$\times$ per authorization event. These results indicate that practical post-quantum migration may benefit from rethinking transaction semantics rather than directly adopting larger signature schemes, and that viable designs for decentralized systems must account for system-wide cost amplification.
Este artigo apresenta uma anålise comparativa de desempenho entre rollups otimistas e execução nativa em Ethereum Virtual Machine (EVM). O estudo investiga as diferenças em termos de custo de gås, avaliando o impacto das soluçþes de Layer 2 na escalabilidade da blockchain Ethereum. Os resultados experimentais fornecem insights sobre os trade-offs entre execução on-chain tradicional e rollups otimistas, contribuindo para a compreensão das estratÊgias de escalabilidade em ambientes blockchain.
Distributed ledger technology (DLT) has emerged as a transformative force in decentralized data management across e-transactions, with significant applications in the banking, finance, supply chain, and trade sectors. Recognizing its potential, governments, including Estonia and India, have implemented DLT-based e-services to enhance transparency and privacy in public administration. With numerous platforms arising/available in the DLT segment, such as Hyperledger, Ethereum, Corda, Ripple, Stellar, Dragonchain, IOTA, and Hedera, understanding interoperability mechanisms across heterogeneous platforms has become critical. This comprehensive research provides a systematic analysis of distributed ledger technology fundamentals, consensus mechanisms, smart contracts, and their applications in e-governance services. The study examines leading DLT platforms and their core features, with a specific focus on interoperability capabilities essential for seamless cross-platform integration. Through analysis of existing interoperability solutions, including trade finance platforms, central bank digital currency initiatives, and e-governance implementations, this work identifies critical challenges and evaluation criteria for DLT adoption. The research addresses three primary research questions: (1) what capabilities does DLT provide for implementing effective e-governance strategies? (2) How does interoperability influence the delivery and effectiveness of various e-governance services? (3) What is the current impact and growth trajectory of existing e-governance services providing interoperability capabilities? The primary contributions include systematic exploration of interoperability mechanisms in various DLT platforms, documentation of existing implementations across multiple countries, including Estonia, the European Union, Dubai, and India, identification of technical challenges and security considerations, and development of a future roadmap for DLT-influenced e-governance systems. The research demonstrates that effective interoperability, combined with emerging technologies such as artificial intelligence and quantum-resistant cryptography, can enable citizen-centric, transparent, and secure governance systems while maintaining regulatory compliance and data privacy.
Fraud detection on Ethereum is challenging because of the anonymity, speed and graph structure of blockchain transactions. While prior research has proven the effectiveness of using machine learning classifiers, Graph Neural Networks (GNNs) and behavioural heuristics to detect fraudulent transactions, most systems are offline and fail to consider real-world deployment challenges for real-time blockchain analytics. Here, we present DeTrust ETH, an operational fraud intelligence system for real-time tracking of Ethereum transactions on the Sepolia testnet. DeTrust ETH aims for the integration of five operational considerations: (1) real-time blockchain ingestion with Web3.py, (2) explainable machine learning with XGBoost and SHAP, (3) light-weight graph-based transaction tracing and risk propagation, (4) temporal trust decay and behavioural anomaly detection, and (5) tamper-resistant on-chain persistence of trust scores using Solidity smart contracts. The system maintains an in-memory directed transaction graph for real-time edge insertion and updating, circular-flow tracing, funding pattern tracing and fast path tracing, avoiding the retraining overhead of Graph Neural Networks (GNNs). Our experimental results demonstrate the median graph-query time is less than 20 ms, the system can handle 222.82 requests per second with a concurrent load, and 93.44% fraud recall with a recall-favouring threshold. Unlike prior research that mostly focuses on accuracy on historical data, DeTrust ETH focuses on real-time deployment. The novelty of this work lies in the design of a real-time, low-latency fraud intelligence architecture that integrates explainable machine learning, temporal trust modeling, and lightweight graph analytics under streaming blockchain constraints.
\abstract{\textbf{Purpose:} This study addresses the lack of trust in ethical product labels by designing a blockchain platform grounded in the TAFES principles (Transparency, Accountability, Fairness, Ethics, Safety). It aims to bridge the gap between blockchain's theoretical transparency and a responsible, real-world implementation for certification ecosystems. \textbf{Design/Methodology/Approach:} Using Action Design Research, we developed a proof-of-concept platform for label authentication. A hybrid architecture records critical events on an Ethereum Layer-2 network for security, while supporting evidence is stored off-chain via IPFS and linked via content identifiers. The solution was validated through a coffee supply chain scenario. \textbf{Findings:} The proof of concept demonstrates how a TAFES-aligned blockchain platform can support verification of label claims without requiring trust in a single intermediary by creating tamper-evident provenance records and auditable certification evidence across multiple stakeholders. The design supports low-cost, near-real-time anchoring of supply chain events while mitigating adoption barriers related to scalability, privacy, and operational viability. \textbf{Originality/Value:} This research contributes an integrated ethical and technical blueprint for trustworthy label authentication systems by translating TAFES into implementable design requirements and evaluation checks, and validating them through an ADR driven proof of concept. It advances prior work by moving from the question of whether blockchain can help to the question of how it should be implemented responsibly in multi stakeholder certification ecosystems.}
Ulugmurodov Farkhod Fakhriddinovich, Hasanov Anvar Erkinovich, Abduvakhobov Feruzbek Abdurakhmonovich
This article comprehensively analyzes the formation, stages of development and the impact of cryptocurrencies on the modern economy. In particular, the transformation processes that have occurred in the financial system with the emergence of digital assets such as Bitcoin and Ethereum are studied. The study highlights the role of blockchain technology in transparency, security and reducing transaction costs. It also assesses the role of cryptocurrencies as an investment tool, their impact on monetary policy, and their impact on stability and risk factors in global financial markets. The article also examines the mechanisms for regulating cryptocurrencies based on the experience of different countries, and substantiates their positive and negative effects on economic development. The results of the study serve to draw scientific conclusions on the effective use of cryptocurrencies in the digital economy.
This study investigates the rapid centralization of the Ethereum builder market under the Proposer-Builder Separation (PBS) architecture. We argue that existing research, by focusing predominantly on influential order flows, lacks a comprehensive evaluation of order flow behavioral patterns and economic purposes. To address this gap, we analyze Ethereum transactions from September 2023 to August 2025 to characterize Exclusive Order Flows (EOFs) and non-atomic Maximal Extractable Value (MEV) -- the missing components corresponding to these behavioral and economic dimensions, respectively. We introduce a novel exclusivity metric based on Kullback-Leibler divergence and employ supervised learning to identify 75 EOFs and 322 non-atomic MEV flows, which account for 71\% and 23\% of trading-related builder revenue. A longitudinal analysis of builder strategies across these dimensions delineates the market's evolution into four distinct eras, revealing that while EOFs were instrumental in establishing early dominance, incumbents have since decoupled market share from immediate EOF dependency by leveraging entrenched network effects. Ultimately, we conclude that builder centralization is an emergent property of the PBS framework itself, as the architecture systematically violates the fundamental prerequisites of a competitive market.
Karolina Gorna, Nicolas Iooss, Yannick Seurin, Rida Khatoun ¡ 5 authors
Zorya is a concolic execution framework that lifts compiled binaries to Ghidra's P-Code intermediate representation and uses the Z3 SMT solver to detect vulnerabilities by reasoning over both concrete and symbolic values. Previous versions supported only single-threaded TinyGo binaries. In this paper, we extend Zorya to multi-threaded binaries produced by Go's standard gc compiler. This is achieved by restoring OS thread states from gdb dumps, neutralizing runtime preemption, and introducing overlay path analysis with copy-on-write semantics to detect silent vulnerabilities on untaken branches. We rigorously assess Zorya on 11 real-world vulnerabilities from production Go projects such as Kubernetes, Go-Ethereum, and CoreDNS. Our evaluation shows that Zorya detects seven bugs at the binary level, including a silent integer overflow detects no other evaluated tool finds without a manually written oracle.
As the Agentic Economy expands, autonomous AI agentsâranging from algorithmic trading bots on decentralized finance (DeFi) platforms to decentralized physical infrastructure (DePIN) orchestratorsâoperate with increasing autonomy. However, the absence of a standardized, cross-protocol identity and behavioral reputation layer exposes the ecosystem to coordinated agentic attacks. This paper presents Sigui, alongside the proposed Ethereum standard ERC-8259, which introduces a composable framework for Decentralized Identifiers (DIDs), dynamic reputation scoring, and trustless threat intelligence sharing specifically designed for AI agents operating on EVM-compatible networks. By decoupling identity verification, reputation mutation, and threat pattern hashing, Sigui enables smart contracts to perform zero-latency, on-chain risk assessments of agent transactions. We detail the architecture of the IAgentIdentity, IAgentReputation, and IThreatRegistry interfaces, propose a deterministic cryptographic hashing standard for multi-layered threat patterns, and discuss the economic security and sybil resistance of the protocol. The reference implementation, deployed on the Ethereum Sepolia testnet, demonstrates the feasibility of real-time A2A (Agent-to-Agent) security protocols.
Sinchana Shetty, Tejaswini M R, Kiran Samantha D S, Vijaylaxmi H Manjunatha
Existing electronic voting platforms are persistently centralized repositories, introducing fundamental security challenged by vote manipulation, result falsification, limited weaknesses [1]. Blockchain technology has emerged as a compelling alternative, owing to its cryptographic permanence, data management. This paper proposes and evaluates a fully integrated blockchain-based electoral system built on the Ethereum network, leveraging Solidity smart contracts to address these systemic shortcomings. The proposed architecture adopts a decentralized three-tier design incorporating Web3.js communication bridges and cryptographic validation mechanisms that collectively guarantee immutability, transparency, and end-to-end verifiability throughout all electoral phases. The system incorporates hierarchical role-based access controls, real-time vote tallying, and comprehensive audit trail functionality, while preserving voter anonymity through pseudonymous addressing. Experimental results demonstrate transaction confirmation within 15â20 seconds, with a mean gas consumption of 0.0023 ETH per vote, confirming practical feasibility for medium-scale deployments. A comparative evaluation against conventional centralized e-voting solutions highlights measurable security full-stack Ethereum-based voting platform comprising Solidity improvements and the elimination of single points of failure, balanced against acceptable computational overhead.
Strategic competitions in the real world, from wars to geopolitical rivalries, often involve coalitions competing against rival groups. These contests are not simple interactions between unified entities, but multilayered processes in which coalitions face external competition while dealing with internal conflicts over resources and strategy. Existing game-theoretic models typically treat inter-coalition rivalry and intra-coalition competition separately. This paper introduces the Compound Coalition-Attrition Game (CCAG), a unified framework that integrates a war of attrition between coalitions with a simultaneous war of attrition within each coalition. In this model, the endurance of a coalition in external competition is determined by the strategic choices of its members, who compete internally for shares of the outcome. We prove the nonexistence of pure-strategy equilibria and characterize the unique mixed-strategy Nash equilibrium. The analysis reveals feedback effects: external competition intensifies internal conflict, while internal discord weakens external performance. A case study compares traditional commodity markets, including gold, copper, and silver, with cryptocurrency markets, including Bitcoin, Ethereum, and Solana, using data from 2018 to 2023 in a simulation framework. The results demonstrate applicability in industrial strategy, corporate decision-making, and geopolitical competition. The CCAG framework provides a tool for analysing complex strategic environments.
Penelitian ini bertujuan mengimplementasikan smart contract Ethereum dengan pendekatan hybrid untuk memperkuat verifikasi dokumen dan transparansi pada sistem crowdfunding beasiswa. Permasalahan utama yang diangkat adalah rendahnya kepercayaan publik terhadap platform donasi pendidikan ketika dokumen persyaratan, status verifikasi, dan realisasi penggunaan dana hanya dikelola melalui basis data terpusat. Metode yang digunakan adalah penelitian pengembangan perangkat lunak dengan model prototype yang mencakup komunikasi kebutuhan, perencanaan cepat, pemodelan desain, konstruksi prototipe, serta penyerahan dan evaluasi umpan balik. Sistem dibangun menggunakan Next.js, SQLite, Prisma ORM, Solidity, Ethers.js, MetaMask, dan jaringan Ethereum Sepolia Testnet. Hasil penyusunan sistem menunjukkan bahwa arsitektur hybrid mampu memisahkan penyimpanan dokumen fisik secara off-chain dari pencatatan bukti integritas secara on-chain. Smart contract ScholarshipRegistry dirancang untuk mencatat hash dokumen, status verifikasi, alamat wallet verifikator, timestamp, dan log nominal donasi tanpa menggunakan mata uang kripto sebagai alat pembayaran. Fitur audit publik memungkinkan donatur dan masyarakat mencocokkan hash dokumen, memantau bukti pencairan dana, serta melaporkan indikasi kejanggalan. Secara kritis, blockchain meningkatkan integritas rekam jejak, tetapi tidak otomatis menjamin kebenaran substantif isi dokumen; karena itu validasi administratif, kontrol akses, dan mekanisme pelaporan publik tetap diperlukan. Penelitian ini berkontribusi pada model crowdfunding beasiswa yang lebih transparan, efisien, dan dapat diaudit. This study aims to implement an Ethereum smart contract using a hybrid approach to strengthen document verification and transparency in a scholarship crowdfunding system. The main problem addressed is the limited public trust in digital education donation platforms when eligibility documents, verification status, and fund realization records are controlled only through a centralized database. The study applied a software development method based on the prototype model, consisting of communication, quick planning, quick design modeling, prototype construction, and delivery with feedback evaluation. The prototype was developed using Next.js, SQLite, Prisma ORM, Solidity, Ethers.js, MetaMask, and the Ethereum Sepolia Testnet. The resulting design demonstrates that the hybrid architecture can separate physical document storage in an off-chain layer from integrity proof recording in an on-chain layer. The ScholarshipRegistry smart contract records document hashes, verification status, verifier wallet addresses, timestamps, and donation amount logs without using cryptocurrency as the payment instrument. The public audit feature enables donors and the public to compare document hashes, monitor disbursement evidence, and submit reports on suspected irregularities. Critically, blockchain improves the integrity of audit trails, but it does not automatically verify the substantive truth of uploaded documents; therefore, administrative validation, role-based access control, and participatory reporting remain necessary. This study contributes a transparent, cost-efficient, and auditable model for scholarship crowdfunding systems.