The Global Alliance for Genomics and Health (GA4GH) Beacon protocol lets researchers ask whether a genomic variant has been observed in a participating cohort and receive aggregate variant-level counts. As Beacon networks grow, two privacy risks remain: host institutions can see plaintext queries, and repeated rare-variant queries can support membership-inference attacks. We present bioETH-Beacon, a smart-contract prototype that runs the Beacon "aggregate count" query over encrypted data on a fully homomorphic Ethereum Virtual Machine (fhEVM). Hospitals upload encrypted marker-count entries, authorized researchers submit encrypted marker queries, and the contract returns an encrypted answer that is released, via an off-chain key-management service, only to the requester named in the contract's on-chain ACL. The design is organized as a 3x4 tier-by-query-family grid spanning genotype, sex, age, and phenotype queries, with tiers that trade stronger confidentiality for lower query cost. For genotype paths, the prototype can add bounded on-chain noise to mitigate probing attacks. Experiments on synthetic panels derived from a Polygenic Score (PGS) catalog show the expected scaling behavior and demonstrate that pre-aggregation can substantially reduce query gas when public marker presence is an acceptable trade-off. Overall, bioETH-Beacon provides a research prototype for confidential Beacon-style genomic querying without a trusted compute evaluator.
This study empirically aims to analyze the impact of primary monetary policy stance and transmission mechanisms of the European Central Bank (ECB)âsuch as the total assets of the ECB, long-term interest rate based on the government bond yields, and the EURUSD exchange rateâon major volatile cryptocurrencies like Bitcoin and Ethereum, as well as the leading stablecoin Tether. To this end, the study employs the linear Autoregressive Distributed Lag (ARDL) and the Bootstrap ARDL (BA-ARDL) procedures, robust approaches with limited data in time series analysis. The dataset consists of monthly data over the period from January 2019 to December 2025. We summarize the novel and robust primary empirical results of our study as follows: First, (i) it is revealed that the ECBâs balance sheet expansion has encouraged Bitcoin and Ethereum, yet has also, to a limited extent, suppressed Tether. Secondly, (ii) while the ECBâs long-term interest rate negatively impacts the prices of Bitcoin, Ethereum, and Tether, the negative impact on Tether is relatively weaker. Finally, (iii) the EURUSD exchange rate positively affects Ethereum, while its effect on Bitcoin is not statistically significant. On the other hand, at a 10% significance level, EURUSD has a weak negative effect on Tether. In conclusion, the empirical evidence demonstrates that the primary monetary policy stance and transmission mechanisms of the ECB influence the leading digital assets in distinct ways. Taking our findings into account is crucial for designing the digital euro in terms of financial stability and regulatory framework. Finally, we offer sound policy implications for the ECB based on empirical findings.
Bhutan's GovTech Agency Secretary Jigme Tenzing confirmed that migration of all 800,000 Bhutanese citizens' identity credentials to Ethereum was completed by Q1 2026. King Jigme Khesar Namgyel Wangchuck Prime Minister Tshering Tobgay and Ethereum co-founder Vitalik Buterin attended the launch ceremony. Ethereum protocol governance is conducted by the Ethereum Foundation and decentralised developer community without Bhutanese constitutional participation or veto authority. Bhutan's constitution enshrines Gross National Happiness as a governance mandate. The National AI Strategy 2025 requires AI systems to align with GNH principles. No Bhutanese governance document specifies the constitutional command layer ensuring that Ethereum protocol changes cannot override Bhutanese constitutional governance of national identity infrastructure. When Ethereum upgrades affect how 800,000 citizens' credentials are verified the GovTech Secretary receives advance notice but has no published mechanism to refuse changes incompatible with Bhutanese constitutional requirements. This paper documents the structural gap between Bhutan's GNH constitutional mandate and the sovereign command architecture required to make that mandate technically enforceable over identity infrastructure governed externally.
Gas metering on EVM-compatible blockchains assumes that execution conditions are stable: that the resource mix is constant enough to justify collapsing execution costs into a single scalar with fixed relative prices, and that state drift between submission and execution does not materially alter a transaction's outcome. We measure the extent to which this assumption fails. We present a trace-level measurement study of EVM workloads on Ethereum (L1) and Base (L2) throughout 2025, sampling 3,000 blocks per day per chain. We decompose each transaction into opcode-level execution gas, intrinsic gas, refunds, and persistent state deltas. To measure state sensitivity, we re-execute transactions from September 2025 on older states and record how gas usage and storage access patterns change. We find the resource mix to be far from stable: on Base, storage reads and compute account for 29.2% and 24.3% of execution gas, while Ethereum devotes 34.9% to storage writes. Ethereum's gas limit doubling during 2025 shifted its own profile toward compute-heavier, Base-like patterns. Base also exhibits a higher fraction of cold storage reads (49.7% versus 39.6% on Ethereum). Persistent state growth, a permanent cost priced as a transient one, reaches 456 GB on Base versus 38 GB on Ethereum. Execution outcomes are equally unstable: gas estimates vary across nearby historical states for 46.0% of transactions on Base, compared to 13.9% on Ethereum, with especially high sensitivity for MEV and DeFi activity. Storage access patterns also diverge across states, limiting the effectiveness of access lists and complicating parallel execution. Our work provides an empirical foundation for multi-dimensional gas metering and explicit pricing of state growth. They show that state-sensitive execution behavior complicates workload estimation, directly affecting transaction predictability and user experience.
The digitization of financial markets has produced two classes of platforms that price, in principle, the same state - contingent payoffs: centralized crypto-option exchanges and blockchain-based prediction markets. This paper provides the first option-implied benchmark test of prediction-market pricing for cryptocurrency threshold contracts. For each hour in a matched sample, we compare the Polymarket Yes price with the discounted risk-neutral binary value implied by a listed Binance call option on the same underlying, strike, and maturity, and study the gap between them. In the main September 2023 Bitcoin contract, the mean pricing gap equals 5.6 percentage points across 214 hourly observations (t = 6.46, p < 10^{-9}). Pooling three Binance-compatible Bitcoin threshold markets yields a mean gap of 6.3 percentage points across 287 observations, robust to HAC and block-bootstrap inference. The gap is persistent - with an AR(1) half-life of roughly four hours - yet mean-reverting, consistent with slow information transmission between segmented venues rather than mechanical noise. Cross-sectional regressions reveal that the wedge is largest at low option-implied probabilities and long maturities, a pattern consistent with speculative demand for prediction-market contracts rather than measurement error. A delta-hedged arbitrage proxy remains profitable after conservative transaction costs, though with marginal statistical precision. A Deribit extension on the same three Bitcoin contracts produces a larger pooled gap of 11 percentage points, while a smaller Ethereum exercise yields mixed evidence. The results demonstrate that digital fragmentation of financial markets generates systematic, persistent pricing wedges even for economically identical payoffs.
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 heterogeneousâ with Ethereum using the Ethereum Virtual Machine to execute Solidity contracts with order-execute transactions and pseudonymous ECDSA-based identityâĪ As Fabric runs Go chaincode under an execute-order-validate enforcement modelâ with MVCC conflict detectionâ XâĪ509 certificate-based identity managementâ and an explicit key-value state APIâ smart contracts cannot be written to run on both Ethereum and Fabric without major duplication of effortâ namelyâ maintaining two separate codebasesâ conducting two separate security auditsâ and manually re-implementing complex codeâĪ 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 studiesâ SimpleStorageâ Escrow and SimpleTokenâ we evaluated the translation with respect to four dimensions: feature translation rateâ semantic approximation accuracyâ barrier coverage and compilation successâĪ Out of the 14 categories of Solidity featuresâ 6 (43%) are completely abstractableâ 4 (29%) can be approximated with semantic gaps SG-1 to SG-2â and 4 (29%) are architecturally non-portable at the contract levelâĪ All three Hyperledger Fabric Go chaincodes built using the UIR approach compiled successfully with go buildâ using Go version 1âĪ22âĪ5â showing the feasibility of the approach with GoâĪ 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 periodâs 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 DickeyâFullerâs 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 HÞsler-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 $Ξ$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 LearningâBased Token Minting and CrossâChain Cryptographic Anchoring This archive contains the full nonâprovisional patent submission for a unified digitalâasset lifecycle system integrating reinforcementâlearningâbased token minting, Merkleâstructured ledgering, and synchronized crossâchain 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 blueprintâbased binding mechanism, a formal kernel governed by a unified state equation, and a sovereign ledger enabling longâterm provenance and deterministic replay. A reversible 32âbyte commitment value is computed using a Spongeâ586 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 tamperâevident, multiâconsensus proofs of state. A reinforcementâlearning engine dynamically adjusts minting rates based on realâtime market conditions, behavioral metrics, and systemâlevel variables. The system further supports gasless user interactions (EIPâ2771), zeroâknowledge compliance pathways, federatedâlearning 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, reductionâtoâpractice demonstrations, and crossâchain anchoring methodology associated with U.S. Patent Application No. 19/693,343.
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Blockchain Technology Applications and Security
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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.
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-learningâdriven 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 supplyâdemand fluctuations. Experiments conducted on Ethereum and Farcaster datasets demonstrate that the proposed mechanism improves the Contribution Fairness Index (CFI) by 19.7%â22.4% 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.
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 KwiatkowskiâPhillipsâSchmidtâShin (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 offendersâ 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 Shorâs Algorithm and Groverâs 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(2âŋ) to O(â2âŋ). 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 (0â1 year) contract audits, wallet security reinforcement, and developer education; medium-term (1â2 years) PQC testing on crypto testnets and adoption of quantum-resistant validator nodes and long-term (1â3 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.