Muhammad Tahir, Adem Orsdemir, Usman Khalid, Alptekın Küpçü
The rapid adoption of blockchain technologies has intensified the need for robust security mechanisms in Ethereum smart contracts (SC). Due to immutability, vulnerabilities cannot be patched after deployment, leading to significant financial losses. While traditional static and dynamic analysis tools are widely used, recent research has explored Machine Learning (ML) and Deep Learning (DL) techniques for automated vulnerability detection. However, many existing approaches focus on single-vulnerability detection or suffer from high false positive rates and limited interpretability. To address these challenges, this study proposes an interpretable DL framework for multi-vulnerability detection in SC. The proposed model employs a lightweight One-Dimensional Convolutional Neural Network (1D-CNN) integrated with Integrated Gradients from Explainable AI (XAI) to provide transparent model decisions. SC opcode sequences are transformed into RGB-encoded sequential representations, preserving execution order while enabling efficient feature extraction. This study adopts a multi-class classification setting to evaluate generalization across diverse vulnerability types. The framework is evaluated using the publicly available Messi-Q dataset, containing labeled samples across multiple vulnerability types. Experimental results demonstrate effective multi-class detection, with performance varying across vulnerability types due to dataset imbalance and structural similarities. The model maintains efficiency while providing interpretable insights for selected vulnerability classes. The model provides opcode-level attribution, revealing localized patterns for certain vulnerabilities and more distributed attention for others. These findings demonstrate the practicality of lightweight and interpretable DL methods for scalable SC security analysis.
ABSTRACT TRSP Digital Coin (TDC) — The Next Evolution of Digital Currency: Quantum-Permanent, Physically Unbreakable, Theft-Proof by Physics Built on: Temporal Rotation Security Protocol (TRSP) v3, DOI: 10.5281/zenodo.20324081. First public documentation: May 2026. TDC is not a replacement for Bitcoin, Ethereum, or any existing digital currency. It is the next evolutionary step for the entire field — the first digital currency architecture whose security is grounded not in mathematical complexity but in physical law. Every existing digital currency rests on one assumption: that breaking the cryptographic protection requires more computational resources than any adversary possesses. Quantum computing is dismantling this assumption. Harvest-now-decrypt-later attacks mean every blockchain transaction recorded today remains permanently vulnerable to any future computational advance. TDC responds with a different premise: a signing key that no longer exists cannot be recovered by any computation, quantum or classical, regardless of future advances. TDC inherits the temporal rotation architecture of TRSP v3. Transaction signing keys rotate every 10–100 milliseconds from physical hardware entropy and are permanently destroyed after each rotation. CRATON-anchored ownership proof replaces persistent private key storage: ownership is demonstrated through a one-time physical commitment derived from the unique state of the signing device at transaction time — used once, permanently destroyed, impossible to forge, impossible to extract, impossible to replay. Three attack paths are structurally closed: private key extraction (no stored key exists), quantum key recovery (key destroyed before computation converges), and harvest-now-decrypt-later (signing key permanently gone — no target for any future computation). Part 9 (Identity Without Storage) documents a five-factor distributed identity architecture in which no single factor and no single location holds everything required to authorise a transaction: biometric presence; primary device CRATON anchor; memorised PIN with distress code variant; Remote Guardian Device in a separate geographic location; and time lock with geo-anchor. The distress PIN architecture triggers a silent alert and time-delayed freeze while providing apparent confirmation to an adversary — making the coercion attack structurally ineffective. Wallet recovery requires no seed phrase: a five-step multi-factor re-enrollment protocol using biometric presence, guardian confirmation, and a 72-hour cancellation window replaces the stored backup phrase that represents the primary theft surface of every existing wallet. Part 10 (Real Identity Enrollment) documents a biometric enrollment architecture that exceeds current KYC bank account standards: NFC chip reading of government-issued documents (cryptographic verification against issuing government public key — not photo or scan), live 3D facial biometric with active liveness detection, all-finger fingerprint enrollment, and a CRATON physical moment binding that ties the enrollment to the unique physical state of the enrollment device at that exact moment. Raw biometric data is deleted after enrollment — only a non-reversible binding token is retained. Identity is distributed across three separately held, individually insufficient components: Enrollment Authority, blockchain, and device. No single party holds all three. Legitimate financial privacy is preserved. The enrollment barrier is structurally higher than any existing digital currency. AML, KYC, GDPR, FATF Travel Rule, and sanctions compliance are structural properties, not regulatory overlays. Part 12 (Implementation Roadmap) documents a four-phase deployment pathway modelled on pharmaceutical clinical trial methodology. Phase 1 (Year 1–2): proof of concept with small high-security institutions — private banks, family offices, university research groups — using software-only TRSP daemon and TEE-based CRATON. Phase 2 (Year 2–4): institutional pilot with mid-size financial institutions and government treasury departments — dedicated CRATON hardware module, Remote Guardian architecture, orbital quorum activated above threshold. Phase 3 (Year 3–5): national pilot with CBDC programmes and full jurisdiction regulatory validation — complete five-factor identity, consumer enrollment refined at national scale. Phase 4 (Year 5–10): global rollout — CRATON chip standardisation licensable to semiconductor manufacturers, TLS 1.3 extension standardised through IETF, "Secured by TDC" certification programme. Each phase generates performance data that validates and de-risks the subsequent phase. The worst outcome at any phase is a parameter adjustment — no user loses funds, no system collapses. Part 13 (Digital Estate Architecture) addresses the inheritance problem that every existing digital currency has left unsolved: what happens to assets when the owner dies. Three mechanisms work together. Designated Heir Enrollment: heirs are biometrically pre-registered at wallet setup — enrolled but cryptographically inactive during the owner's lifetime, with no access to balance or transaction history. Death Verification Protocol: succession requires three simultaneous conditions — official government-issued death certificate verified by the Enrollment Authority, 2-of-N Remote Guardian confirmation, and a mandatory 90-day waiting period during which the owner can cancel with biometric presence. Dead Man's Switch: an optional owner-defined inactivity window that triggers Guardian alerts and initiates the succession protocol if neither owner nor Guardian responds within the alert window. For owners without designated heirs: charitable designation to enrolled organisations, institutional estate trustee, or deliberate coin retirement. Owner financial privacy is maintained completely during lifetime. Post-succession historical access is configurable by the owner at setup. Novel contribution NC-TDC-17 is placed on the public record as defensive prior art. Privacy architecture clarification: the default state of every TDC wallet is complete financial anonymity. Identity disclosure is exclusively owner-initiated — the owner may selectively disclose individual transactions for tax certification, charitable donation receipts, regulatory compliance, or proof of funds. No court order, no government authority, and no institution can access wallet identity or transaction history without the owner's willing biometric participation. The three-part distributed binding token architecture makes bypass technically impossible — not merely legally prohibited. This is not a policy decision. It is a physical property of the architecture enforced by the requirement for live owner biometric activation of the device component. Novel contributions NC-TDC-13 (Geographic Coercion Evidence Layer), NC-TDC-14 (Phased Validation Rollout Architecture), NC-TDC-15 (Owner-Controlled Selective Disclosure), NC-TDC-16 (Enrollment-Anchored Privacy Architecture), and NC-TDC-17 (Digital Estate Architecture) are hereby placed on the public record as defensive prior art. Novel contributions NC-TDC-1 through NC-TDC-17 are placed on the public record as defensive prior art: quantum-permanent transaction signing; CRATON-anchored ownership proof; Generation 4 digital currency architecture; five-factor distributed identity; distress PIN with silent alert; Remote Guardian Device architecture; seed-phrase-free recovery protocol; biometric-CRATON enrollment binding; privacy-preserving three-part identity distribution; AML/KYC compliance by architecture; tiered enrollment framework; orbital CRATON quorum for sovereign transfers. The architectural frameworks described in this concept represent technical design guidelines only and are not legal advice, regulatory guidance, or binding specifications. Actual implementation in any jurisdiction will require adaptation to applicable local law including inheritance law, data protection regulation, anti-money laundering legislation, and financial services licensing requirements. Version 2 introduces four formal additions. Mathematical Formalization (Part 6.1.5): the transaction pipeline is formally specified as a four-step ephemeral verification protocol — KDF ephemeral key generation from physical entropy (sk_eph, pk_eph) = KDF(E_phys); Non-Interactive Zero-Knowledge Proof binding the ephemeral public key to the enrollment token without exposing persistent identity credentials; hardware-enforced destructive readout with thermodynamic irreversibility anchored in Landauer's Principle (ΔW ≥ n·k_B·T·ln2); and deterministic public-parameter-only ledger validation. Formal Threat Model (Part 4.5): three adversary classes formally defined — quantum network attacker (A_network, unbounded computational resources), malware/hardware attacker (A_local, full OS compromise), and coercion attacker (A_kinetic, physical duress) — with security proofs against each. Part 7b (AI-to-AI Micropayment Architecture, NC-TDC-21) documents the application of TDC quantum-permanent transaction signing to autonomous AI agent commerce. Every existing AI payment mechanism — static API keys, server-stored crypto wallets, centralised billing — represents a permanent credential attack surface vulnerable to quantum decryption. TDC coin eliminates this: each AI-to-AI transaction generates a CRATON commitment from the hardware entropy of the transacting inference node at that exact millisecond, used once to sign the micropayment and immediately destroyed. No stored credential on any server. Five new markets are documented: pay-per-inference settlement (USD 50B+ annual market), CRATON-anchored API key replacement, autonomous multi-agent revenue distribution at service delivery, AI training data micropayments for individual contributions, and cross-agent behavioural monitoring via the AI Guardian Layer at machine speed. The AI Guardian Layer (NC-TDC-19) monitors t
Sohel Akhtar, Murat Karakuş, Rukiye Savran Kiziltepe
Smart contracts are a fundamental building block of blockchain platforms such as Ethereum, yet their development and auditing require specialized expertise and remain highly error-prone. The immutability of deployed smart contracts significantly amplifies the consequences of coding mistakes and security flaws. Recent advances in Large Language Models (LLMs) have shown promise in automating software development and code analysis tasks; however, the reliability of LLM-generated smart contracts and their effectiveness in vulnerability auditing, particularly for Solidity, remains insufficiently explored. In this paper, we present a systematic and automated evaluation pipeline to comparatively assess the performance of open-source LLMs in two critical phases: (i) smart contract generation from natural language specifications, and (ii) smart contract auditing for vulnerability detection. We benchmark multiple open-source models under consistent experimental settings and analyze their correctness, security awareness, and robustness against insecure outputs. Our findings expose significant performance gaps across models and tasks, revealing strengths and limitations of current open-source LLMs in supporting secure smart contract development. This study provides practical insights for researchers and practitioners seeking to apply LLMs to blockchain programming and security assessment.
Drissia Ennagoura, Kamal El Kehal, Safae Merzouk, BERDAI ABDELHAMID · 8 authors
Prices of cryptocurrencies are tough to forecast due to their high volatility and susceptibility to abrupt market changes. This paper compares four models—ARIMA, Prophet, LSTM, and XGBoost—to predict Ethereum (ETH) prices on three horizons: 15 minutes, 1 hour, and 1 day. We compared all four models concerning Root Mean Squared Error (RMSE) from the historical ETH data. The outcome shows XGBoost performs best on short-term forecasting with an RMSE of 352 in 15-minute and 357 in 1-hour data, surpassing LSTM and ARIMA. For the daily prediction, Prophet shows competitive performance with an RMSE of 941, whereas ARIMA is generally stable. The findings conclude that the ideal model depends on the forecasting horizon, and for short-term trading, using XGBoost is advisable, while Prophet is advisable for longterm forecasting. The study provides valuable recommendations to investors and researchers seeking effective cryptocurrency prediction software.
Ankit Sitaula, Ashraf Uddin, John Ayoade, Nam H. Chu · 5 authors
Counterfeit and unsafe medicines pose significant risks to patient safety and undermine trust in healthcare systems. This paper presents ACTMeds, a blockchain-supported pharmaceutical traceability and recall platform that considers pharmaceutical supply chain requirements and public health operational needs relevant to the Australian Capital Territory (ACT). The system integrates Ethereum smart contracts, developed using Ganache, with a React-based web application providing regulator, operator, pharmacy, and auditor interfaces, alongside a public verification portal leveraging QR and GS1 barcodes. In addition, role-based access control is enforced across the medicine lifecycle, including manufacture, custody transfer, dispensing, and recall, with immutable on-chain events generated to support auditability and accountability. To balance transparency with confidentiality, the platform prototypes a zero-knowledge (ZK) recall mechanism in which regulators can cryptographically prove that recall conditions meet predefined policy requirements without disclosing sensitive incident details. Threat modeling was conducted using the STRIDE framework, and security evaluation combined static application security testing (Solhint and ESLint) and dynamic testing. The paper further discusses deployment options, cost considerations, ZK recall performance analysis, ethical implications, and future enhancements. Security testing validated the platform’s resilience, with no high-severity vulnerabilities identified and medium-severity issues related to HTTP security headers addressed. The results indicate that a regulator-led, privacy-preserving, tamper-evident ledger can improve medicine authenticity verification and recall responsiveness while maintaining compliance and data protection obligations.
Muhammad Farooq Shaikh, Muhammad Saad Iqbal, Davide Piaggio
Introduction Public-sector grant disbursement and fundraising systems are vulnerable to corruption due to limited transparency, weak auditability, unauthorized approvals, and poor traceability of financial transactions. This study proposes a blockchain-based framework to improve accountability and monitoring in public financial management. Methods An Ethereum-compatible ERC-20 test network was used to develop a blockchain-enabled architecture integrating smart contracts, decentralized application (DApp) components, and a Proof-of-Authority (PoA)-based governance mechanism. The framework was evaluated using simulation-based testing, transaction validation scenarios, corruption-detection workflows, and indicative gas-cost analysis under controlled conditions. Results The proposed framework enabled tamper-resistant audit trails, timestamping, and end-to-end traceability of grant transactions. A hierarchical governance model incorporating multi-level institutional approvals reduced unilateral control and improved transaction accountability. Simulation-based evaluation demonstrated improved transparency, auditability, and transaction monitoring efficiency, while maintaining relatively low indicative gas costs. Discussion The findings suggest that blockchain-supported governance mechanisms may strengthen transparency and accountability in public-sector financial systems under controlled conditions. The proposed framework demonstrates the potential of combining institutional governance structures with blockchain-based validation to support secure and traceable grant management and fundraising processes.
Abstract The traditional Byzantine quorum-system model assumes a pre-existing, global agreement on the set of quorums (typically defined as the sets consisting of more than two-thirds of the participants). This assumption is problematic in permissionless systems, which strive to allow anyone to join or leave the system dynamically. While proof-of-stake permissionless systems like Ethereum require newly joining participants to register into the system, other permissionless systems like the Ripple Ledger or the Stellar network allow participants to join the system without synchronization by forgoing agreement on the set of quorums. This results in what we call a heterogeneous quorum system, where each participant has its own, personal set of quorums. An important question is to determine under what condition is it possible to solve synchronization problems like reliable broadcast or consensus in a heterogeneous quorum system. In this work, we show that the traditional quorum intersection and quorum availability conditions are not sufficient in heterogeneous quorum systems. Moreover, we propose quorum subsumption, a new condition which, together with quorum availability and quorum intersection, is sufficient to allow solving reliable broadcast and consensus. Finally, we propose protocols for reliable broadcast and consensus in heterogeneous quorum systems that satisfy quorum subsumption. In particular, we present a practical consensus protocol called Satrapy which in contrast to abstract consensus protocols uses finite state and messages.
Penelitian ini berfokus pada pengembangan framework autentikasi tanpa kata sandi (passwordless) berbasis Web3 yang diimplementasikan pada platform mobile guna mengatasi kerentanan metode tradisional terhadap serangan phishing dan brute force. Framework yang diusulkan mengintegrasikan aplikasi mobile dengan backend Node.js/Express.js dan smart contract standar ERC-5192 pada jaringan Ethereum Sepolia Testnet sebagai representasi identitas digital Soulbound Tokens (SBT) yang permanen dan non-transferable. Demi menjaga privasi, sistem ini menerapkan teknologi Zero-Knowledge Proof (ZKP) berbasis zk-SNARKs skema Groth16 menggunakan Circom dan SnarkJS yang dieksekusi di sisi klien (client-side browser) menggunakan WebAssembly (WASM), serta dipadukan dengan struktur data Merkle Tree tingkat kedalaman 20 dan mekanisme nullifier untuk mencegah replay attack. Hasil pengujian menunjukkan tingkat keberhasilan autentikasi mencapai 100% dari 50 kali percobaan. Pemindahan beban komputasi sirkuit ZKP (5.359 konstrain) ke sisi klien terbukti efisien dengan waktu eksekusi komputasi lokal jika diakumulasikan dari tahap awal koneksi wallet (0,8 detik), pembuatan witness (1,2 detik), pembuatan proof (4,8 detik), hingga verifikasi smart contract (210 ms), maka Total Authentication Time adalah sebesar 6,3 detik. Nilai ini membuktikan kelayakan framework ini sebagai solusi manajemen identitas yang aman, privat, dan responsif.
Ivan Vynyavskyy, Stefan Kitzler, Bernhard Haslhofer, Aviv Yaish
Modern Portfolio Theory (MPT) prescribes how to maximise the return of an asset portfolio for a given level of risk. The optimal trade-off between return and variance defines the efficient frontier. Whether actual cryptoasset portfolios approximate this prescription and whether proximity to the frontier translates into realised performance remain difficult to test at large scale in traditional markets due to their opaque nature and the inaccessibility of data. As we show, public blockchains make these questions measurable: every token transfer is recorded, thus enabling complete portfolio reconstruction for every account at any point in time. We leverage this transparency to reconstruct cryptoasset portfolios for over 116M Ethereum accounts across the full chain history (2015-2025), measure their distance to the constrained efficient frontier, and quantify how deviations translate into realised performance. Here we show that market entry timing, not allocation choice, is the dominant predictor of realised cryptoasset returns. On-chain wealth is highly concentrated and portfolios are pervasively under-diversified, with single-asset holdings accounting for 83.35% of accounts. Two-asset portfolios sit closest to the efficient frontier defined by their held assets, a proximity that reflects the narrowness of their opportunity set rather than deliberate optimisation. Passive market-capitalisation weighting outperforms every MPT optimisation strategy in median realised return, and entry month alone explains 70-79% of the variance in returns, far exceeding the contribution of allocation choice. Mean-variance optimisation therefore appears neither descriptive of observed behaviour nor prescriptively useful in the cryptoasset domain, even if MPT retains its value as a normative benchmark.
The burgeoning prevalence of Ethereum phishing behavior has iCSUR-2025-0155mposed substantial constraints on the advancement of blockchain finance, resulting in losses of more than $7.7 billion to date, so it is urgent to detect it in time. Currently, available detection methods usually focus on the spatial features within transaction graphs. These methods often employ shallow mining techniques on small samples. As a result, they may overlook certain aspects of interaction patterns, such as temporal behavior. Additionally, their data mining capability is limited due to the small sample sizes. In this study, we propose a graph contrastive learning framework to enrich features of accounts behavior patterns with restricted samples to overcome these limitations. Firstly, we construct an Ethereum interaction graph with the multi-graph involving more temporal information centered with labeled nodes and lighten it with our strategy. Secondly, to comprehensively characterize the accounts pattern, we design the encoder part with the GAT-LSTM model based on attention mechanism fusing statistical features , fine-grained temporal behavioral features and graph structural semantic features . Thirdly, to moderate the sparsity of phishing nodes, we employ data augmentation and contrastive learning to fully mine sparse node information. Moreover, we carried out an in-depth experimental evaluation. The CMD-EPD approach, boasting an F 1 -score of 0.87, outperformed all comparison methods. We also executed a thorough case study to analyze phishing accounts phenomenological indicators which back up the superiority of our framework.
Dappfort is a blockchain-focused Web3 development company that helps businesses harness the power of decentralized technologies to build secure, scalable, and future-ready digital solutions. Headquartered in Madurai, India, with additional presence in London, Dappfort works across a broad range of industries — including finance, healthcare, gaming, retail, and supply chain — delivering tailored blockchain and Web3 applications to startups, enterprises, and global organizations. The company’s core services include the design and development of decentralized applications (DApps), crypto exchanges (centralized and decentralized), crypto wallets, NFT marketplaces, DeFi platforms, token creation, smart contract development, and enterprise Web3 integration. Dappfort also expands into related areas such as Web3 e-commerce, AI-powered blockchain solutions, and metaverse experiences, supporting clients from strategy and consulting through deployment and ongoing support. With expertise in major blockchain networks like Ethereum, Solana, Binance Smart Chain, and others, Dappfort positions itself as a full-stack partner for businesses aiming to enter or grow in the decentralized digital economy. While the company promotes a strong innovation- and security-oriented approach, external reviews on third-party platforms show mixed feedback from users about project delivery and quality.
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Internet of Things and AI
Blockchain Technology Applications and Security
Innovations and Analysis in Business and Education
Daniel González Cortés, Monomita Nandy, Suman Lodh
Abstract This research analyzes the performance and interconnectedness of major global stock market indices and decentralized finance assets, specifically cryptocurrencies, over the period from 2015 to 2025. The study includes indices such as the S&P 500 and Nasdaq Composite from the United States, the FTSE 100, DAX, and CAC 40 from Europe, and the Nikkei 225 from Japan, and two more indices from China and India representing different economic regions. Additionally, Bitcoin and Ethereum are included to assess the impact of decentralized finance on traditional financial indices and asset allocation strategies. By employing Artificial Intelligence algorithms like ConvLSTM, the research measures the dynamic asset allocation and volatility management through an interconnected spillover matrix. The findings reveal that integrating ConvLSTM enhances the understanding of the interconnectedness between cryptocurrencies and traditional assets, offering improved diversification opportunities due to their low correlation, decentralization, and inflation-hedge characteristics. The study’s results suggest that investors can make more informed decisions regarding dynamic asset allocation in high-volatility portfolios, providing indicators of rising systemic risk and market stress.
Mingshun Ye, Dezhi Han, Chin‐Chen Chang, Mingdong Tang · 6 authors
The rapidly expanding Ethereum ecosystem has driven the flourishing of decentralized applications, but has also brought increasingly severe security risks. Ponzi scheme, in particular, pose a grave threat to platform security and user assets by luring investors with promises of high returns. The current detection methods generally suffer from limitations such as insufficient feature extraction, reliance on a single information source, and poor robustness. To address these challenges, this paper proposes a novel Multi-View Multi-Modal Fusion Framework with Large Language Models for Ponzi scheme detection on Ethereum, named MF2LLM. We first model the contract opcode sequence as an opcode chain graph and design a Time-Stamped Graph Encoder (TS-GE) to capture local temporal dependencies and execution flow relationships between opcodes. Concurrently, we construct an opcode semantic hypergraph based on semantic categories and design a Semantic-Weighted Hypergraph Encoder (SW-HGE) to model higher-order co-occurrence patterns and global associative features. Furthermore, we propose the Opcode Sequence Lightweighting (OSL) method, which significantly compresses the length of opcode sequences while preserving core control logic and semantic information. This provides high-quality structured input for information fusion. To this end, we perform multi-modal instruction fusion on multi-source heterogeneous features and employ LoRA to fine-tune LLMs. This enables the model to achieve cross-modal semantic reasoning and behavioural pattern recognition. Through extensive experimental validation on real-world datasets, MF2LLM demonstrates stable and superior detection performance even under conditions of highly imbalanced sample distributions. Compared to existing state-of-the-art approaches, our method outperforms across all metrics, achieving an ACC of 99.43%, Precision of 96.57%, Recall of 97.06%, and an F1-score of 96.81%. The efficiency and practical value of MF2LLM in detecting Ponzi schemes on Ethereum contribute to enhanced security for the decentralized application ecosystem. The codes are publicly available on Github: https://github.com/yemisua/MF2LLM.
Agricultural trade in developing countries continues to depend on informal agreements, multi-tier intermediary networks, and centralized payment mechanisms, resulting in payment delays of 30–90 days, information asymmetry, and weakened bargaining power for smallholder farmers. This paper presents AgriHandshake, a blockchain-based smart contract platform enabling direct crop trading between farmers and vendors through an automated escrow payment mechanism deployed on the Ethereum network. The system employs a hybrid architecture that stores cryptographic state hashes and escrow logic onchain while offloading trade metadata and delivery documentation to the InterPlanetary File System (IPFS), reducing average transaction gas costs to approximately 85,000–210,000 gas units per operation. A Solidity-based escrow contract enforces a structured four-state machine (CREATED→FUNDED→DELIVERED→COMPLETED/DISPUTED) with a 72-hour automatic payment-release timer implemented via block.timestamp. Experimental evaluation on the Ethereum Sepolia testnet demonstrates average smart contract function execution latency under 15 seconds, end-to-end trade confirmation within 3–8 minutes including IPFS upload, and strong resistance to reentrancy and front-running attacks. Comparative analysis against eNAM, FarMarket, and AgriOnBlock confirms that AgriHandshake is the first platform to combine a dedicated escrow payment guarantee, decentralized off-chain storage, and a farmer-centric usability model within a single deployable framework
The rapid digitalization of global infrastructure has amplified the vulnerabilities inherent in centralized cloud storage systems, where single points of failure, administrative access abuses, and external cyberattacks routinely compromise sensitive data.Traditional cloud architectures rely on centralized trust models that frequently succumb to data breaches and censorship.To address these critical flaws, this paper proposes De-Drive, a zeroknowledge, hybrid decentralized storage application (DApp) that seamlessly integrates Web3 architecture with robust cryptographic protocols.De-Drive leverages a hybrid storage model: heavy file payloads are stored off-chain on the decentralized InterPlanetary File System (IPFS) via Pinata nodes, while the immutable reference links-Content Identifiers (CIDs)-are permanently logged on the Ethereum blockchain (Sepolia Testnet) utilizing a custom, highly gas-optimized Solidity smart contract.To solve the inherent privacy flaws of public IPFS networks, De-Drive implements strict client-side Advanced Encryption Standard (AES) cryptography.Files are converted to Base64 strings and encrypted locally within the React frontend utilizing a user-defined symmetric key prior to network transmission.This architecture ensures absolute zero-knowledge storage; neither the IPFS nodes hosting the data nor the blockchain observers auditing the ledger can decipher the underlying content without the specific decryption key.The proposed architecture successfully resolves the blockchain storage trilemma by delivering a decentralized, immutable, and strictly private data vault, demonstrating significant improvements in cost-efficiency and security over both traditional cloud services and purely on-chain storage alternatives.
The article examines digital financial assets as an instrument of investment portfolio diversification in the Ukrainian investment context. The study argues that Bitcoin and Ethereum should not be assessed through general statements about financial innovation, but through their measurable contribution to portfolio return, volatility and risk-adjusted performance. The empirical part is based on an annual scenario model for 2020–2025 and compares portfolios with 0%, 1%, 3%, 5% and 10% exposure to BTC and ETH. The benchmark portfolio includes domestic government bonds, the USD/UAH currency component, gold and the S&P 500 as a global equity benchmark, while the local Ukrainian equity segment is interpreted cautiously because of its limited liquidity. The results show that portfolios with 1–5% exposure to BTC and ETH improved risk-adjusted efficiency compared with the baseline portfolio. The P3 scenario provided the most balanced relationship between return growth and risk growth, while P5 generated a higher average annual return with still acceptable volatility. The P10 scenario produced the highest geometric average annual return but almost tripled volatility compared with the baseline portfolio, making it more suitable for an aggressive investor profile. The article concludes that digital financial assets may have practical value only as a limited high-risk addition to a diversified portfolio, not as a stable hedging instrument or a substitute for traditional instruments.
Every BFT consensus protocol uses collision-resistant hashes to compare validator state. Collision resistance destroys distance: two validators agreeing on 19 of 20 transactions produce unrelated hashes, indistinguishable from validators sharing nothing. This forces three design constraints across the BFT literature: validators must synchronize state before voting, agreement quality cannot be measured until votes are counted, and hierarchical committees must be large enough for independent BFT, limiting tree depth. This paper introduces distance-preserving transaction digests, a primitive that replaces collision-resistant hashes with commutative vector sums in 8-dimensional space. The primitive has three properties hashes lack: distance is proportional to disagreement, weighted means are exact, and set differences are identifiable via bloom filter diff. We demonstrate three applications: a two-phase BFT protocol (Proxima) that achieves single-round finality when validators agree; tree-structured consensus with groups of 10 validators (vs 128 in Ethereum), enabled because distance filtering replaces per-group BFT; and cross-shard consistency verification at 128 bytes per shard pair, replacing the per-transaction coordination of two-phase commit. Safety is proved: fewer than N/3 Byzantine validators cannot cause conflicting finalization, independent of Phase 1 clustering or tree topology. At N =100,000, Proxima Tree uses 2.2x fewer messages than HotStuff (a structural property unaffected by parallelism). Single-core finality is 0.9s vs 18s for HotStuff; multi-core BLS narrows but does not eliminate this gap.
Sara Batal, Safae Belamfedel Alaoui, Said Hraoui, Mohammed Berrada
Cloud-delivered artificial intelligence (AI) is a technology that raises many problems related to access control, such as API misuse and difficulties in managing interactions with other AI models. Centralized access control offers insufficient oversight, is prone to major administrative or system failures, and has inherent weaknesses, for example central IAM dependencies and a single point of failure. We propose a decentralized access control system that leverages blockchain and smart contracts to automatically execute agreements between parties to authenticate and maintain a history of access to the attributes of the deployed AI models. Our architecture enables access control through immutable systems, which promises that the rules and access control records can never be modified. We used an experimental methodology to preserve a real system close to production and test its performance and behavior under high load. This methodology focuses on cross-chain access control security through RoleBased Access Control that will be used in our smart contracts. From the results obtained in the previous test phase, when load was increased and RBAC was implemented in the smart contracts, we were able to measure the trade-offs between gas cost and access latency within the computational resource. Resource consumption and architecture stability were measured in milliseconds as the number of processed transactions increased. Our architecture is still applicable to cloud-based AI services. Although we gained a high gas cost from the test results due to integrating RBAC into the Ethereum smart contract, we proved that access control is guaranteed in the cloud.
As sustainability plays an increasingly important role in finance, understanding its influence on emerging assets such as cryptocurrencies is essential for portfolio management. This article analyzes the relation between sustainability and cryptocurrency returns. To address and reveal complexity in relationships, we use non-linear machine learning methods. We find that sustainability variables, like energy consumption and environmental attention, are important return determinants. The greenness of a cryptocurrency measured by the consensus mechanism is a group-specific differentiation variable for the most sustainable cryptocurrencies with a positive impact on their returns. The economic relevance of their green consensus mechanism materializes primarily in the lower tail of the return distribution by providing downside protection. We detect a clear upward trend in complexity, with maxima during COVID-19 and the change of Ethereum’s consensus mechanism from Proof of Work to the more environmentally friendly alternative Proof of Stake. These findings underline the importance of considering sustainability factors in cryptocurrency investment decisions, offering new insights for investors as well as policymakers.
Manuel Lagos Rodríguez, Hilda Romero Velo, Álvaro Leitao Rodríguez, Javier Pereira Loureiro · 5 authors
Ethereum use as a decentralized platform for executing smart contracts has driven the adoption of standards that optimize interoperability in industrial environments. Ethereum Requests for Comments (ERC) establish uniform patterns for smart contracts, facilitating their integration and operation in network nodes, which are essential for industrial applications such as supply chain management or process automation. However, this standardization can propagate vulnerabilities or inefficiencies in critical systems if the contracts are not optimized, affecting the reliability of industrial processes. Since operations in Ethereum consume computational resources (measured in gas) with associated economic costs, poor design can lead to significant losses in industrial settings. This paper evaluates the efficiency and security of ERC standards by examining their functional diversity and technical complexity. Thus, it analyzes existing implementations to identify common errors and proposes improvements to enhance the robustness and optimization of three of the most popular standards: ERC-20, ERC-1400 and ERC-3643. The ultimate goal is to support the effective adoption of ERCs in industrial applications. Considering the Ethereum network incentives on lower complexity logic and the obtained results, it is advisable to use simple standards, which also reduce error risks and ease maintainability.