This study presents the design and implementation of a blockchain-based decentralized portfolio management system that enables secure, immutable, and transparent storage of user records. The system is developed using Ethereum smart contracts and evaluated within a testing environment consisting of Remix IDE, Ganache, and MetaMask. The proposed architecture allows authorized actors to create records while enabling users to access and verify their data through blockchain-based identity mechanisms. Experimental results, based on gas consumption and insertion-time measurements, demonstrate that although smart contract deployment incurs relatively high initial costs, routine operations such as record insertion and retrieval remain efficient and predictable. The findings highlight the practical feasibility of the proposed system, while also revealing challenges related to scalability, cost variability, and system usability.
Ethereum and Hyperledger Fabric are architecturally heterogeneous‚ with Ethereum using the Ethereum Virtual Machine to execute Solidity contracts with order-execute transactions and pseudonymous ECDSA-based identity․ As Fabric runs Go chaincode under an execute-order-validate enforcement model‚ with MVCC conflict detection‚ X․509 certificate-based identity management‚ and an explicit key-value state API‚ smart contracts cannot be written to run on both Ethereum and Fabric without major duplication of effort‚ namely‚ maintaining two separate codebases‚ conducting two separate security audits‚ and manually re-implementing complex code․ This thesis aims to both design and test a Universal Intermediate Representation (UIR) for the migration of smart contract logic from Ethereum to Hyperledger Fabric in a structured, auditable and repeatable way. In the spirit of Design Science Research (DSR) (Peffers et al., 2007), this study investigates five portability barriers to the extent that they can be identified (PB-1 to PB-5) and relates them to six design requirements (R1 to R6). Based on this, two-stage prototype pipeline is created in Python, a front-end based on Solidity contracts and a back-end which generates Hyperledger Fabric Go chaincode. In three canonical case studies‚ SimpleStorage‚ Escrow and SimpleToken‚ we evaluated the translation with respect to four dimensions: feature translation rate‚ semantic approximation accuracy‚ barrier coverage and compilation success․ Out of the 14 categories of Solidity features‚ 6 (43%) are completely abstractable‚ 4 (29%) can be approximated with semantic gaps SG-1 to SG-2‚ and 4 (29%) are architecturally non-portable at the contract level․ All three Hyperledger Fabric Go chaincodes built using the UIR approach compiled successfully with go build‚ using Go version 1․22․5‚ showing the feasibility of the approach with Go․ The thesis is not about the fact that UIR is a production ready tool. The pipeline has no total automation; in the 3 case studies, the processing of function bodies was done manually in Stage 1. Also, the prototype currently only approximates 256-bit integers. The actual contribution is conceptual: It suggests a structured, auditable way to detect and overcome portability issues from Ethereum to Hyperledger Fabric. The master thesis consists of 94 pages; it contains 9 figures, 28 tables, 2 appendices, and 42 references.
Traditional philanthropic organizations often suffer from lim ited transparency, where donors have minimal visibility into how their contributions are utilized after donation [1,14]. To addressthisissue, this paper presents NGO-Chain, a hybrid Web3 platform designed to im prove accountability and transparency in charitable fund management. The proposed system utilizes a milestone-based conditional escrow mech anism in which donated funds are locked within blockchain smart con tracts and released incrementally only after administrative verification of uploaded proof documents stored on the InterPlanetary File System (IPFS) [4,5]. The architecture combines React-based frontend interfaces, Spring Boot middleware, decentralized IPFS storage, and Ethereum/Polygon smart contracts to create a scalable hybrid infrastructure capable of supporting real-time public transaction monitoring [14,12]. In addition, the platform integrates donor reputation tracking and blockchain-backed transaction auditing to strengthen trust between donors and NGOs [6,7]. By com bining decentralized financial management with milestone verification workflows, NGO-Chain provides a secure and transparent framework for milestone-driven charitable donations while reducing dependency on cen tralized trust mechanisms.
A current, urgent problem is whether the price behavior pattern of significant quantities of digital assets reflects a single direction trend line or multiple phases that exhibit different structures, adjusted inter-asset relationship differences, and changes in management systems, given the growing importance of digital assets in investment portfolios and collateral holdings, exchange-traded funds (ETFs), new forms of financial activities, and system risks over the period from 2020 through 2025. Because of this period’s post-pandemic recovery, speculative overextension, sharp decline, stabilization, and the re-entry of large-scale institutions into practice, these changes in prices are more clearly identified under such a context. Empirically, this study integrates descriptive statistics, rolling volatility analysis, augmented Dickey–Fuller’s unit-root test, segmented trend regression model with structural breaks, and vector autoregression (VAR) for return interactions. Based on these bases, both Bitcoin and Ethereum have demonstrated a relatively strong direction of continuous appreciation, together with quite considerable regime-specific instability. The log-price series is non-stationary, but the daily return series is stationary; so a level model is appropriate for medium-term trend analysis, and returns-based models can be applied more flexibly at shorter timespans. The segmented trend-regression analysis shows that close to peaks, such as those that occurred in 2021 for a long period, the 2022 correction, and the resumption of investment in 2024, are relatively distinct from the overall linear change pattern across all time periods. Both Bitcoin and Ethereum display pronounced contemporaneous co-movement, but they show no substantial lags via VAR or Granger causality tests conducted in the context of time-varying parameters. This study employs an integrated empirical research approach based on various perspectives to explore the long-term structural adjustment and near-instantaneous cross-market relationship dynamics, as well as regulatory mechanisms within a systemic context.
Dr. B. Indira Reddy, Naga Siva Jyothi Kompalli, Dr. Rohita yamaganti, CH Sai Saketh · 6 authors
The ongoing digital evolution in the healthcare sector has increased the demand for reliable and secure systems to manage medical records. Conventional centralized storage methods are vulnerable to security threats such as data breaches, unauthorized usage, and potential data alteration, which can compromise patient confidentiality and data integrity. To overcome these challenges, this work presents a blockchain-enabled medical record management system designed to provide secure and tamper-resistant data storage. The proposed system is implemented as a decentralized web application, utilizing React.js for the user interface and Web3.js or Ethers.js to enable interaction with the blockchain network. Smart contracts written in Solidity are deployed on the Ethereum platform to handle record management and enforce strict access permissions. User authentication is facilitated through MetaMask, ensuring a secure and decentralized method of identity verification. Healthcare information, including patient records, diagnoses, prescriptions, and treatment details, is maintained on the blockchain to guarantee transparency and immutability. The system empowers patients by allowing them to control access to their data, including granting and revoking permissions for healthcare providers. Tools such as Truffle and Ganache are used during development for efficient testing and deployment. In summary, the proposed solution improves data security, privacy, and accessibility, offering a dependable and scalable approach for managing healthcare records in modern digital environments.
Cryptocurrency markets are prone to violent, synchronised drawdowns, challenging the claim that a basket of crypto-assets offers genuine internal diversification. Because standard covariance-based metrics fail to capture asymptotic tail dependence, they systematically understate systemic risk and overstate diversification benefits precisely when markets crash. This study maps the conditional dependence structure of the cryptocurrency market directly in the joint tails, isolating direct extremal linkages from those mediated by the rest of the system. We analyse the daily returns of the thirteen largest cryptocurrencies over a sequence of 89 overlapping windows spanning late 2021 to 2025. We apply dynamic Hüsler-Reiss graphical models of extremes, estimated separately for joint crashes and rallies, and benchmark them against a Gaussian graphical model of ordinary co-movement. The results reveal a near-complete and stable lower-tail graph, an upper tail that thins over time to re-form sectoral structures, and the dissolution of ordinary token categories into a single block anchored by a Bitcoin-Ethereum core. These findings imply that intra-crypto diversification fails on the downside, standard risk models underestimate market-wide crash probabilities by roughly eight-fold, and dynamic extremal graphs offer a superior tool for systemic risk monitoring.
Modern blockchain state management faces a critical scalability bottleneck: maintaining cryptographic commitments over hundreds of millions of entries becomes computationally prohibitive. Ethereum's transition to Verkle Trees: polynomial commitment accumulators reducing proof sizes from O(width * depth) to O(depth) via constant-size IPA vector commitments, is a critical step toward stateless operation. Yet, current implementations exhibit pathological characteristics that burden home validators. We identify four inefficiencies in the reference go-verkle implementation \cite{kaur2025goverkle, kaur2025goethereum}: (1) phantom node creation during non-existent account deletion; (2) 64-byte database keys triggering excessive LSM-tree compaction; (3) redundant memory copying in proof deserialization; (4) a Proof of Absence wire format incompatibility causing non-deterministic serialization. We present Fractional Verkle Trees (FVT), a hypertree decomposition partitioning global state into N independent sub-accumulators coordinated by a Merkle commitment tree, achieving improved cache locality, zero-lock-contention goroutine-parallel commitment computation, and faster root recomputation (91 $μ$s vs $\sim$500 ms). We address each inefficiency via existence checks, 32-byte SHA256 node references, zero-copy reference-counted buffers, and HashMap-based lexicographic deduplication. Benchmarks on Apple M1 Pro show 57\% heap allocation reduction (566,760 to 242,004 bytes per 10K proofs), parallel insertion at 2,433 ns/op, and network-wide elimination of 4.85 PB/year across 6,000 full nodes, advancing the Ethereum stateless roadmap.
Data availability is a fundamental bottleneck in modern blockchain networks. Most blockchain systems rely on a full-replication model, which requires downloading of a full block to verify its availability. This model does not scale with block size because every node must handle large volumes of data, leading to slower block propagation, duplicated data transfer, and longer consensus agreement. This issue is well-known in Ethereum, where layer-2 rollups publish data directly into the chain. To overcome, Ethereum adopts Data Availability Sampling (DAS) to let nodes keep only a small fragment of the data while still ensuring availability. Prior work on DAS has focused on cryptographic foundations. Meanwhile, the peer-to-peer network layer that provides Byzantine-tolerant and scalable mechanisms for discovery and routing of DAS fragments is underexplored. We propose CDA, a new design for DAS based on coded distributed arrays that leverages network coding to ensure both robustness and efficiency. Our evaluation study compares CDA to RDA, the latest DAS development of Ethereum, showing an improvement of several times better.
Metode hybrid Autoregressive Integrated Moving Average dengan Support Vector Regression (ARIMA-SVR) merupakan salah satu metode untuk peramalan deret waktu yang mampu menangkap pola linear dan nonlinear secara bersamaan. Penelitian ini bertujuan menerapkan model hybrid ARIMA-SVR untuk meramalkan harga Ethereum dan mengetahui akurasi model hybrid ARIMA-SVR yang diperoleh pada harga Ethereum. Data yang digunakan yaitu data harga penutupan Ethereum pada rentang waktu 11 Desember 2020 sampai 10 Desember 2025, penelitian dimulai dengan membagi data menjadi data training dan data testing dengan tiga skema pembagian data yaitu 70%:10%, 80%:20%, dan 90%:10%. Hasil penelitian menunjukkan model terbaik yaitu ARIMA(2,1,2)-SVR dengan parameter terbaik sebesar 0.8125, parameter sebesar 5, dan parameter sebesar 0.125 pada skema pembagian data 90% data training dan 10% data testing. Akurasi model hybrid ARIMA(2,1,2)-SVR ditunjukkan oleh nilai Mean Absolute Percentage Error (MAPE) yang diperoleh yaitu 2.83% untuk data training dan 2.72% untuk data testing. Kata Kunci : ARIMA, SVR, Hybrid ARIMA-SVR, Ethereum The hybrid Autoregressive Integrated Moving Average with Support Vector Regression (ARIMA-SVR) method is a time series forecasting method capable of capturing both linear and nonlinear patterns simultaneously.This study aims to apply the ARIMA-SVR hybrid model to forecast Ethereum prices and determine the accuracy of the ARIMA-SVR hybrid model obtained for Ethereum prices. The data used consists of Ethereum closing prices from December 11, 2020, to December 10, 2025, the study began by dividing the data into training and testing sets using three data partitioning schemes is 70%:10%, 80%:20%, dan 90%:10%. The results indicate that the best model is the ARIMA(2,1,2)-SVR with optimal parameters = 0.8125, = 5, and = 0.125, under the 90% training dan 10% testing data split. The accuracy of the ARIMA(2,1,2)-SVR hybrid model is demonstrated by the Mean Absolute Percentage Error (MAPE) values obtained, which are 2.83% for the training data and 2.72% for the testing. Keywords : ARIMA, SVR, Hybrid ARIMA-SVR, Ethereum
System and Method for Reinforcement Learning‑Based Token Minting and Cross‑Chain Cryptographic Anchoring This archive contains the full non‑provisional patent submission for a unified digital‑asset lifecycle system integrating reinforcement‑learning‑based token minting, Merkle‑structured ledgering, and synchronized cross‑chain cryptographic anchoring. The invention establishes a deterministic, mathematically governed framework for creating, operating, and verifying digital asset states across heterogeneous blockchain networks including Bitcoin, Ethereum, and Solana. The system introduces a blueprint‑based binding mechanism, a formal kernel governed by a unified state equation, and a sovereign ledger enabling long‑term provenance and deterministic replay. A reversible 32‑byte commitment value is computed using a Sponge‑586 invariant and anchored to Bitcoin via Taproot tweaks and OP_RETURN payloads. Parallel anchoring events emit the authenticated Merkle Mountain Range (MMR) root on Ethereum and Solana, producing tamper‑evident, multi‑consensus proofs of state. A reinforcement‑learning engine dynamically adjusts minting rates based on real‑time market conditions, behavioral metrics, and system‑level variables. The system further supports gasless user interactions (EIP‑2771), zero‑knowledge compliance pathways, federated‑learning simulations, and deterministic state reconstruction through Kolmogorov integrity scoring and synthesis restoration. This archive includes the complete specification, mathematical formulations, alternative embodiments, and references to supporting research hosted on Zenodo. It documents the developmental lineage, reduction‑to‑practice demonstrations, and cross‑chain anchoring methodology associated with U.S. Patent Application No. 19/693,343.
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Physical Unclonable Functions (PUFs) and Hardware Security
This is an independent research project with publicly released, reproducible code (not a peer-reviewed publication). We ask which class of behavioural signal drives machine-learning detection of fraudulent Ethereum accounts: graph, transaction (value/volume), or temporal (timing) features, on 9,307 labelled accounts. Crucially we distinguish degree-count graph features from true graph-topology features (PageRank, k-core, clustering, degree centrality) reconstructed from a 242,518-node, 1.65M-edge transaction graph. Transaction-value features are the strongest single class (PR-AUC 0.93), but true graph-topology significantly outperforms degree counts (PR-AUC 0.84 vs 0.70, p<1e-6) and adds the most on top of transaction features; PageRank is the single most informative feature. The topology result survives a time-respecting leakage audit (features rebuilt from each account's earliest 70% of transactions). All code, data pointers, figures, and tests are released.
The purpose of this study is to examine the potential safe-have properties of the two most popular cryptocurrencies, i.e., Bitcoin and Ethereum, against equites, government bonds and gold. To do so, the paper makes use of a daily dataset ranging from 2018 to 2022 acknowledging both the COVID-19 and the potential halving effect in the cryptocurrency market. To robustly assess the research question, the paper employs a quantile GARCH model with non-parametric diagnostics, dynamic Local Projections and rolling window estimations for robustness. The findings of the paper suggest that both assets act as diversifiers against equities and against each other, whereas the halving effect is statistically insignificant and the COVID-19 effect is statistically significantly positive only for the returns of Ethereum. The results imply that cryptocurrencies could contribute to portfolio diversification under stress market conditions.
Michael Kah Ong Goh, Yu-Xian Cheng, Check-Yee Law, Connie Tee · 6 authors
Traditional ticketing systems often suffer from major drawbacks such as ticket fraud, duplication, inflated resale prices, lack of transparency, and centralized control over transactions. These issues result in reduced trust and limited flexibility for both event organizers and ticket buyers, especially in unregulated secondary markets. To address these gaps, this paper presents the design and development of a Decentralized Ticketing System (DTS) using Web3 technologies. The system leverages Ethereum blockchain, smart contracts written in Solidity, and NFT-based ticket issuance to ensure security, transparency, and verifiable ownership. Features include wallet-based login via MetaMask, multi-ticket purchasing, QR-based validation, controlled resale pricing, and seller revenue withdrawal. Smart contract reliability is enhanced using OpenZeppelin libraries and tested with Mocha and Chai. By decentralizing control and automating ticket processes, the proposed DTS enhances current practices by offering a more secure, tamper-proof, and user-centric ticketing alternative that mitigates fraud and enables transparent peer-to-peer interactions. The architecture of this system integrates a decentralized storage and interaction layer that connects the blockchain smart contracts with a web-based user interface which allow organizers to create events and sell tickets while buyers can securely browse, purchase, and manage their digital assets. The system also demonstrates how blockchain-based ticketing can improve traceability, reduce intermediaries, and support fairer event ecosystems for stakeholders across industry.
This research discusses the incorporation of IoT with blockchain technique to enhance the efficiency of smart farming systems, particularly focusing on plant disease classification, pest detection, and smart irrigation. The study aims to develop a secure and effective IoT-based smart farming framework using the Ethereum blockchain to store and transmit data, and a Hybrid Convolution Adaptive Recurrent MobileNet (HC-ARMNet) model for predictive analytics, optimized by the Improved Secretary Bird Optimization (ISBO) algorithm. The research employs IoT sensors to acquire real-time data, which is then stored in the Ethereum blockchain to ensure security. The HC-ARMNet model, combining 1D/2D convolutions with recurrent connections, processes this data for pest detection and irrigation management. The ISBO algorithm is leveraged to fine-tune the technique's parameters. Datasets used: The proposed system utilizes three standard datasets for evaluation. The PlantifyDr Dataset is used for classifying plant disease, and the Pest Detection Dataset is used for recognizing pests. Also, for the smart irrigation process, the significant field images are collected manually. The accuracy, precision, and FNR rates of the ISBO-HC-ARMNet-aided plant disease classification are 94.16%, 94.2% and 5.87%. At the same time, the ISBO-HC-ARMNet-based pest detection process's accuracy, sensitivity, and specificity are 93.78%, 93.79% and 93.76%, respectively. In addition, the ISBO-HC-ARMNet-based smart irrigation task's MSE is 3.21, SMAPE is 0.03, and MASE is 30.23. Thus, the designed system showcases promising performance over classical approaches in terms of accuracy and error rates for plant disease classification, pest detection, and smart irrigation. The research concludes that the IoT-aided smart farming framework with blockchain and the HC-ARMNet model provides a robust solution for secure and efficient agricultural management. The system's predictive capabilities provide accurate and timely data analysis, facilitating to the improvement of precision agriculture. Future work will focus on improving the system with advanced feature extraction strategies to reduce processing time.
With the accelerated marketization of data factors, achieving fair contribution evaluation, privacy-preserving verification, and dynamic incentives in decentralized environments has emerged as a critical challenge. Existing studies exhibit a structural tension between privacy protection and verification transparency, while lacking adaptive mechanisms for non-independent and identically distributed (Non-IID) data scenarios. To address these issues, this paper proposes a collaborative trading framework integrating zero-knowledge proofs, personalized federated learning, and reinforcement learning. The framework employs zk-SNARKs to construct non-interactive proofs, thereby resolving the verification-privacy dilemma. A meta-learning–driven personalized aggregation scheme is introduced to correct valuation bias under Non-IID data distributions, and a deep Q-network (DQN) agent is deployed to enable dynamic incentive responses to market supply–demand fluctuations. Experiments conducted on Ethereum and Farcaster datasets demonstrate that the proposed mechanism improves the Contribution Fairness Index (CFI) by 19.7%–22.4% over the strongest baseline, achieving a Verification-Utility Ratio (VER) of 24.6. Under a collaboration scale of N = 20, market vitality entropy increases to 0.75 (baseline: 0.41), effectively suppressing monopolistic tendencies. Moreover, despite the introduction of proof mechanisms, the estimated additional on-chain verification and consensus latency per round is approximately 13 s, calibrated against empirical benchmarks. This work provides a verifiable trading mechanism for data factor markets that jointly ensures privacy, fairness, and efficiency, supporting secure data circulation in domains such as healthcare and finance.
Purpose This study investigates the volatility spillover dynamics between carbon credit market represented by European Union Allowance (EUA) futures and major cryptocurrencies, Bitcoin (BTC) and Ethereum (ETH), during the 2020–2024 period. It aims to understand whether these assets, despite their difference in regulatory and structural features, exhibit interconnected volatility pattern and particularly under crisis or shock conditions. Design/methodology/approach The article employs a two-step econometric approach. First, the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model is used to estimate time-varying return correlations among EUA, BTC and ETH. And second, the Diebold–Yilmaz (2012) spillovers index based on forecast error variance decomposition is applied to quantify the sizes, directions and evolution of volatility spillovers across markets. Findings The results reveal significant but uneven and time-varying volatility spillovers between carbon and cryptocurrency markets. Spillover intensity becomes more prominent, especially during major crisis periods such as the COVID-19 pandemic, the Russia–Ukraine war and the FTX collapse. Spillovers are asymmetric and regime-dependent. ETH emerges as the main net volatility transmitter, while BTC exhibits a near-neutral and regime-dependent role, alternating between transmitting and receiving shocks. EUA futures remain largely insulated, with only limited outward volatility transmission even under extreme market conditions. These findings suggest the presence of conditional and crisis-driven spillover linkages between green and digital assets. Originality/value This is among the first studies to empirically examine the volatility transmissions between carbon credit and cryptocurrency market using advanced econometric tools. It contributes to the emerging green -digital finance literature by identifying dynamic and directional interdependency across these evolving asset types.
SAMUEL OBOH, Boniface Dondo, S. Yakura Bassa, Gambo I. Bature
Ethereum, a leading digital asset by market value, has gained increasing attention from investors and researchers because of its high price volatility and market unpredictability. This study forecasts Ethereum cryptocurrency daily closing prices using the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) methodology, drawing on data from January 1, 2019, to December 31, 2025. Stationarity analysis via the Augmented Dickey-Fuller ADF and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests confirmed that first differencing was required to render the series suitable for the modeling. Through systematic model identification, estimation, and comparison of ten candidate ARIMA specifications, the ARIMA(1,1,0) model emerged as the optimal fit, yielding the lowest information criterion values of Akaike information criterion, Bayesian information criterion (AIC = 24,943.883; AICc = 24,943.84; BIC = 24,955.12). Residual diagnostic tests, including the Ljung-Box test for serial correlation, the Autoregressive Conditional Heteroskedasticity (ARCH-LM) test for heteroscedasticity, and the Shapiro-Wilk test for normality, confirmed that the model residuals are free of serial dependence, although they exhibit time-varying volatility and non-normal distribution, features commonly associated with financial time series. The fitted model was subsequently applied to generate 30-day ahead forecasts with 95% confidence intervals, revealing relatively stable price expectations in the near term alongside progressively widening prediction bands that reflect growing uncertainty over longer horizons. These findings underscore the practical utility of the parsimonious ARIMA(1,1,0) model as a transparent and accessible tool for short-term Ethereum-price forecasting and investment risk assessment.
Yuanyuan Zhang, N. J. Lord, Stephen Chan, Jeffrey Chu · 5 authors
This study examines the relationship between global phishing crime and cryptocurrency-market conditions, with a specific focus on Ethereum. Using monthly data from January 2016 to December 2022, we analyse the returns of global phishing crime numbers together with six Ethereum financial metrics relating to transactions, trading volume, and price impact. We employ quantile regression, quantile-on-quantile regression, and Granger causality in quantiles to examine whether the relationship between Ethereum market indicators and phishing activity varies across different market states. The results reveal a state-dependent relationship. Large increases in phishing crime numbers are strongly associated with large increases in Ethereum transaction activity, average transaction price, and transaction quantity, while implicit transaction cost is predominantly negatively associated with phishing activity, particularly at the upper quantiles. These findings suggest that phishing risk is most pronounced during extreme market conditions and may be shaped by both reward-enhancing market activity and cost-enhancing transaction frictions. To interpret these patterns, we develop an incentive-based criminogenic mechanism in which Ethereum market conditions affect phishing activity through offenders’ expected payoff. We identify two mediating channels: a monetisation-frictions channel, operating through liquidity, price impact, slippage, and transaction costs; and an attention/information-asymmetry channel, operating through volatility, speculative attention, fear of missing out, and user vulnerability. The findings provide initial evidence that cryptocurrency-related phishing is not only a technical cybersecurity issue, but also a market-sensitive phenomenon shaped by financial incentives, liquidity conditions, and behavioural vulnerability. These insights can support regulators, law enforcement agencies, and cryptocurrency platforms in developing adaptive early-warning and prevention strategies.
Muhammad Husnul Hamdala, Erna Kumalasari Nurnawati, Yuliana Rachmawati Kusumaningsih, Suparyanto
The threat to blockchain security has become increasingly critical in the era of quantum computing. This study analyzes the potential risks of quantum computers against crypto by simulating five attack scenarios using Shor’s Algorithm and Grover’s Algorithm. Shor is employed to exploit weaknesses in the elliptic curve digital signature algorithm (ECDSA) by factoring large integers to obtain private keys, while Grover accelerates the search for valid hashes or inputs, reducing complexity from O(2ⁿ) to O(√2ⁿ). The testing environment was built on a local Ethereum network using Ganache, with attack scripts implemented through Node.js, Python (Qiskit), and Hardhat. The results demonstrate that both quantum algorithms can compromise smart contracts, proof-of-stake consensus mechanisms, user wallets, and public key cryptography. Attacks were successfully carried out without original private keys, showing potential for asset theft, consensus manipulation, replay attacks, and increased computational load that could disrupt network availability. Although the simulations were conducted on classical hardware, the findings provide a realistic perspective that large-scale quantum computers will significantly increase the risk of blockchain security breaches. These findings emphasize the urgency of transitioning to post-quantum cryptography (PQC) through approaches such as lattice-based cryptography, hash-based signatures, and layered authentication. Implementation strategies can be divided into three phases: short-term (0–1 year) contract audits, wallet security reinforcement, and developer education; medium-term (1–2 years) PQC testing on crypto testnets and adoption of quantum-resistant validator nodes and long-term (1–3 years) full migration from ECDSA to PQC through key rotation and infrastructure updates.
Quantum computing poses a real, broad-based, but bounded and substantially mitigable threat to Bitcoin and Ethereum. We separate the two quantum algorithms that public discussion routinely conflates: Shor's algorithm breaks the elliptic-curve signatures (ECDSA over secp256k1, BLS over BLS12-381) that authorize spending, whereas Grover's algorithm does not meaningfully threaten proof-of-work mining, which is protected by a merely quadratic speedup, fault-tolerant per-operation costs, a square-root parallelization wall, and difficulty adjustment. Folding hardware scaling, the falling resource requirement, a fault-tolerance readiness lag, and expert surveys into a single Monte-Carlo forecast yields a wide, bimodal arrival distribution for a cryptographically relevant quantum computer: about a one-in-six chance by 2035, near 30% by 2040, and about 60% by 2050. Exposure is concentrated and mostly migratable: of Bitcoin's roughly six million quantum-exposed coins only about 2.3 million are irreducibly at risk, while 50 to 65% of Ether sits at key-revealed accounts that can adopt post-quantum signatures. A timely migration beats even an optimistic 2035 machine, so the binding constraint is governance, not technology. A survey of the top twenty cryptocurrencies finds none fully post-quantum. Reproducible models accompany every quantitative claim.
The paper aimed to investigate the statistical relationship between Bitcoin prices and Ethereum trading volumes, as well as to create a simple predictive model for Ethereum trading volumes based on Bitcoin prices. To perform Spearman’s rank correlation analysis and to construct an artificial neural network (ANN) model, daily closing prices of Bitcoin in USD and daily trading volumes of Ethereum were utilized. The timeframe covered by the data starts May 1, 2020 and ends November 22, 2025. In this study, Ethereum volumes were treated as the dependent variable, while Bitcoin prices served as the independent variable. The findings indicate a significant, moderate, positive correlation between Bitcoin prices and Ethereum volumes, and the ANN model successfully predicted Ethereum volumes with a high level of accuracy. These results reinforce existing evidence regarding the relationships among cryptocurrencies. Furthermore, by confirming the efficacy of artificial neural networks (ANN) in predicting trends within the cryptocurrency market, the study also makes a methodological contribution. In addition, the study also offers a simpler modelling approach that highlights the significance of bilateral interactions among major cryptocurrencies through a single-input model. Based on the impressive performance of the ANN model, exchanges, fintech companies, and investment firms could incorporate lightweight machine-learning systems into their forecasting tools to provide real-time analytics with minimal processing requirements.
This paper presents a blockchain-based electronic voting system designed to address the persistent challenges of transparency, security, and integrity in democratic electoral processes. Traditional voting systems in countries like Nepal suffer from vote manipulation, ballot rigging, logistical inefficiencies, and limited public trust. To overcome these limitations, this work proposes a decentralized e-voting application built on the Ethereum blockchain, leveraging smart contracts for tamper-proof vote recording and enforcement of voting rules. The system incorporates multi-factor authentication, combining facial recognition via OpenCV with Voter ID and Date of Birth verification to ensure only eligible voters participate. MetaMask wallet integration enables secure blockchain transactions, while Web3.js facilitates real-time interaction between the frontend and the deployed smart contracts on Ganache. The methodology encompasses data collection, voter authentication, smart contract deployment, and result retrieval. This paper offers a scalable and cost-effective alternative to conventional voting methods, with future scope for public Ethereum deployment and expanded biometric authentication.
Bhutan has deployed a blockchain-based national identity system on Ethereum and issued a gold-backed sovereign token, while simultaneously developing a National AI Strategy 2025 that requires AI governance to align with Gross National Happiness constitutional principles. Ethereum protocol governance is conducted by the Ethereum Foundation and developer community without Bhutanese constitutional participation. This paper documents the constitutional command gap between Bhutan's GNH governance philosophy and the technical infrastructure on which its digital sovereignty depends, and presents the Fijishi Sovereign Identity Framework, Sovereign Algorithmic Immunity Doctrine, and Institutional Failover Charter as the constitutional command layer that ensures GNH principles govern AI and digital systems regardless of the underlying protocol architecture.
This paper presents a comparative study between two leading blockchain platforms—Hyperledger Fabric and Ethereum—with emphasis on their architectural design, performance characteristics, and security mechanisms in the context of enterprise applications. The study aims to identify key differences between permissioned and public blockchain models, focusing on scalability, consensus efficiency, and data confidentiality. A controlled experimental environment was developed using Docker-based deployments for both platforms, and performance was evaluated through Hyperledger Caliper using standardized workloads. Metrics such as transactions per second (TPS), latency, resource consumption, and failure rates were analyzed under varying network sizes. The results indicate that Hyperledger Fabric achieves significantly higher throughput (≈900 TPS) and lower latency (<200 ms) compared to Ethereum (≈25 TPS, ≈1 s latency), due to its deterministic Raft consensus and modular architecture. Ethereum, however, demonstrates superior decentralization and transparency suitable for public and decentralized applications. The findings highlight that both platforms are complementary: Hyperledger Fabric is optimized for controlled, high-performance enterprise use, while Ethereum excels in open, trustless environments.