Adaora. A, Obayi, Caroline Asogwa, Blessing .C. Uzo
This study investigates traditional health care delivery systems to eliminate current inefficiencies by creating a decentralized appointment and referral management system using Web 3.0 technology, blockchain, smart contracts, and Decentralized Identity (DID) compatible with scalable cloud storage. In a series of multi-agent simulations run on the Ethereum and Polygon Testnets, the performance of the system under simulated high-load traffic scenarios was tested. The simulation results showed consistent transaction latencies (285 ms average), high throughput rates (34 appointments per second), and low errors rates (1.4%). Another innovation of this study was the development of a hybrid architecture that enables the storage of cryptographic hashes associated with medical records on-chain, while keeping patient data encrypted on off-chain servers. This allows the immutability and auditability of the data while still maintaining compliance with GDPR and HIPAA regulations by enabling patient data to be deleted from the system entirely. As a result, this system was significantly more secure, transparent, and operationally efficient compared to current centralized systems. These findings confirm and support the potential of decentralized technologies for Scalable, Trustworthy Medical Service Delivery of the Data.
Real-world asset tokenization is often presented as a mechanism for improving the liquidity of traditionally illiquid assets. However, on-chain representation and secondary-market liquidity are distinct outcomes. This paper examines whether tokenized real-world assets exhibit meaningful observed liquidity and identifies the token characteristics associated with higher market activity. Using token-level data from RWA.xyz and supplemental contract-level observations from Etherscan, the study constructs an Ethereum-based monthly panel of non-stablecoin real-world assets across three prominent categories: U.S. Treasury-backed tokens, gold-backed commodity tokens, and private-credit-related tokens. Liquidity is measured using turnover, active addresses, and an active-month indicator. The empirical design combines descriptive statistics, non-parametric group tests, and exploratory panel regressions suited to short and sparse token histories. The results show substantial heterogeneity across asset categories. Gold-backed tokens exhibit broader holder bases and more persistent on-chain activity than many Treasury and private-credit-related products, while outstanding asset value alone does not reliably predict observed liquidity. The paper contributes to the literature by developing a clearer empirical measurement framework for real-world-asset liquidity and showing that tokenization and liquidity should be analyzed as distinct outcomes.
Decentralized Finance (DeFi) has introduced new security challenges due to its open, permissionless, and pseudonymous nature, which has increased the risk of fraud and phishing activities. This thesis first presents a comprehensive study of 284 DeFi platforms to examine their architectural, functional, and security-related characteristics. Building on this ecosystem-level analysis, the thesis develops a behavior-centric multiclass detection framework using Ethereum transaction data. The framework integrates legitimate, fraud, and phishing activities into a unified dataset and evaluates several traditional and deep tabular learning models, including TabNet, GANDALF, and NODE. The results show that deep tabular models outperform conventional baselines, with NODE achieving the strongest overall performance. Feature importance analysis highlights gas usage, nonce behavior, transaction frequency, and wallet activity as key indicators of malicious behavior. Overall, this thesis demonstrates that behavior-based Ethereum transaction features combined with deep tabular learning can support more effective and scalable DeFi threat detection.
We describe a verification pipeline that takes production Rust cryptographic code and produces machine-checked correctness proofs in Lean 4. The pipeline combines three components: symbolic extraction tools (Charon and Aeneas, or Hax) that lift Rust into Lean 4; formal cryptographic specification libraries (ArkLib and CompPoly, from the Verified zkEVM project) that provide the mathematical targets; and AI provers (Aristotle from Harmonic AI and Aleph from Logical Intelligence) that close the resulting proof obligations. Every proof is checked by the Lean kernel, so AI output cannot compromise soundness. Within the scope of the Ethereum Foundation's zkEVM Verification Project, we applied the pipeline to cryptographic primitives in Plonky3 (FRI folding, Mersenne31 and KoalaBear field arithmetic, Horner polynomial evaluation) and RISC Zero (Merkle inclusion verification). In addition, Aleph authored proofs of two bounds-style theorems in Plonky3's compute_log_arity_for_round that previously stood as sorry. The paper describes the architecture, walks through a running example based on Aleph's two proofs, reports which classes of proof obligations AI closed and which required manual work, and discusses the engineering gaps we encountered: Lean 4 toolchain drift across tools and specific Aeneas/Hax extraction limits. We also document concrete missing lemmas, tactic gaps, and code-generation friction points discovered during proof development. We hope this contribution lowers the barrier to adoption of formal verification and facilitates more effective use of AI in this pipeline. The result is a working pipeline for formal verification of Rust, with kernel-checked proofs and reproducible artefacts.
The proliferation of AI-generated synthetic media poses a critical threat to the integrity of digital evidence in legal and forensic contexts. Existing deepfake detection systems typically address a single modality and provide no mechanism for tamper-proof evidence preservation. We present DeepFake Forensics AI, a unified platform that detects synthetic media across image, video, and audio modalities, identifies generative architecture fingerprints, and anchors forensic evidence immutably on the Ethereum blockchain. Our system trains four independent neural networks from scratch: an EfficientNet-B4 image detector (AUC = 0.9868), a Bidirectional LSTM video detector (AUC= 0.9628), an ECAPA-TDNN audio detector (EER = 18.63%), and a novel GAN fingerprinting module (accuracy = 99.88%) that identifies the generative architecture behind a fake image. Evidence files are hashed with SHA-256, stored on IPFS via Pinata, and registered on-chain via a Solidity smart contract with role-based access control. The platform provides a React frontend and FastAPI backend suitable for deployment in forensic and legal workflows. To our knowledge, this is the first system to unify multi-modal deepfake detection with blockchain-based chain-of custody management.
Abstract As AI agents evolve into autonomous economic actors, verifiable and legally binding identity frameworks become critical. This paper presents Ricardian-TEA , a novel architecture combining Triple-Entry Accounting (TEA), Ricardian Contracts, and Distributed Ledger Technology to assign âLegal-Technical Identitiesâ to AI agents. We provide rigorous mathematical foundations: a Ricardian-TEA Integrity Theorem proving that constraint enforcement, non-disputability, and identity binding hold with overwhelming probability under standard cryptographic assumptions, and a Cyber-Chama Convergence Proposition characterising reputation-based trust dynamics. The framework ensures GDPR compliance via Zero-Knowledge Architecture and Crypto-Shredding. Proof-of-concept implementations on Ethereum Sepolia and Bitcoin SV testnets demonstrate chain-agnostic applicability, achieving at worst 1.4 s latency per transaction while maintaining 100% auditability of AI transactions.
The realms of metaverse and finance have been completely revolutionized through the application of blockchain technology. This technology offers convenience, transparency, security, and decentralization to the virtual world while overcoming the inefficiencies of the traditional financial landscape. The present chapter highlights the various services offered by blockchain technology in the metaverse, such as security, interoperability, and ownership status, especially in the area of finance. A digital landscape with a large number of users sharing information from various geographical locations using an internet connection is called the metaverse. Blockchain technology ensures transparency and security in the metaverse by building trust among users. The users get complete authority over their digital assets, thereby enhancing their participation in the digital realm. In the financial sector, the traditional financial system is transformed through the introduction of blockchain technology, such as cryptocurrencies like Bitcoin and Ethereum, decentralized finance (DeFi), smart contracts, and asset tokenization. Cryptocurrencies, like Bitcoin 506 and Ethereum, provided by blockchain technology, are digital currencies that ensure secure transactions and do not need any authority to act as a middleman. Decentralized finance (DeFi), based on decentralization, facilitates peer-to-peer financial transactions. Smart contracts are automatically executed agreements or contracts made using blockchain technology, which makes the process of transactions easier. Asset tokenization is a representation of the value of assets as digital tokens. However, blockchain technology also presents challenges in various services, necessitating the need to address them to improve opportunities.
Nick Riccardi, Rodney Paul, Andrew Weinbach, Hamid Ekbia
This study examines behavioral pricing patterns in blockchain-based fantasy sports markets by analyzing non-fungible token (NFT) sales on Sorare.com, a decentralized platform where users buy, sell, and compete with digital player cards. Each NFT corresponds to a licensed professional soccer player and is traded in a secondary market on Ethereum-based infrastructure. We construct a unique dataset by linking player performance data with transaction-level NFT sales across multiple seasons, focusing on the Rare and Super Rare card tiers. To isolate the effects of recent performance on secondary market prices, we develop a matching algorithm that connects NFT transaction records to individual card IDs based on sale timestamps and card-specific metadata. This structure allows us to test whether player NFTs are priced efficiently, or whether recent on-field success leads to temporary price inflation consistent with hot hand bias. Our results align with the presence of performance-chasing or hot hand pricing, where recent form leads to a temporary inflation of perceived value. Our framework builds on prior work examining the hot hand hypothesis in traditional sports betting marketsâincluding studies by Camerer (1989), Brown and Sauer (1993), and Paul and Weinbach (2005, 2011, 2014)âand extends this behavioral lens to tokenized digital assets.
Connects to 16 breakthroughs. AUM Inflow Despite Price Slide - TipRanks From GoogleNews (271,272,274,275,276,277,278,279,285,286,287,288,290,291,293,294). Avg score: 0.24 Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
Muhammad Umar Janjua, Akshaya Mani, UÄur Ćen, Daniel Kaiser
Privacy and anonymity of validators, especially regarding IP address linkability, are essential to protect the Ethereum network from various attacks. Network-level attacks, such as DoS, can interrupt validators and affect the overall security of the Ethereum network. Correlating the IP addresses of validators with their identities, along with knowledge about their action slots can be exploited by attackers to cause network delays, MEV exploitation, and finality risks. Therefore, ensuring the unlinkability of a validator's IP and identity is crucial for maintaining the network's trust and resilience. In this techreport, we first provide a review of the existing network and consensus layer techniques that have been proposed for maintaining validator privacy in the Ethereum blockchain. Secondly, we evaluate a Tor-based protocol named Tor push that helps unlink validator identities (IDs) from their nodes' IP addresses, thereby making it difficult to determine any end-to-end correlation between validator IDs and IP addresses of validators' beacon nodes. To evaluate the effectiveness of Tor push, we present a working, deployed proof-of-concept (PoC) implementation in the Nimbus Ethereum client. Our PoC deployment pushes attestations, aggregations, and block proposals over Tor to the Goerli testnet. Furthermore, we also analyse the security and latency of Tor push. Our experimental results suggest that Tor can be incorporated into the existing Ethereum network with a tolerable latency overhead of 613.82 ms on average and without compromising the overall network performance while enhancing the location privacy of validators in the Ethereum network.
Onur Eren Arpaci, Florian Kerschbaum, Sujaya Maiyya
Encrypted cloud storage can hide data contents but still leak sensitive information through access patterns. ORAM addresses this by hiding access patterns, but existing ORAM systems are too inefficient to deploy in practice. We present Cloak, an oblivious storage system that dramatically improves performance by leveraging a simple, widely observed property of real workloads: temporal locality, where recently accessed items are more likely to be accessed again soon. Instead of trying to make server accesses look perfectly uniform, Cloak makes server traffic follow a fixed, "recentness-biased" pattern and then uses real queries to fill as much of that traffic as possible. When the workload exhibits temporal locality, Cloak achieves overheads as low as $1.1\times$ over a non-oblivious and unencrypted baseline. Importantly, this heuristic affects only performance, not security. We evaluate Cloak on Netflix click-stream and Ethereum transaction traces, achieving 165,000 and 157,000 operations per second, respectively, on a single machine.
This study investigates the dynamic relationship between network activity and transaction fees in the Ethereum blockchain by analysing the interaction between Gas Used and Gas Price through a multivariate time series model. The objective is to determine whether variations in network demand influence short-term gas price fluctuations. Daily data of Gas Used and Gas Price were transformed into different logarithmic forms to ensure stationarity. The Augmented DickeyâFuller test confirmed that both variables are stationary at the five percent significance level, with ADF statistics of â6.21 for Îlog (Gas Used) and â7.12 for Îlog (Gas Price), and p-values below 0.001. The Vector Autoregression model was estimated with an optimal lag length of fourteen days, selected using the Akaike Information Criterion, reflecting the persistence of network and fee dynamics. The results of the Granger causality test indicate a unidirectional causal relationship from Gas Used to Gas Price, with an F-statistic of 3.72 and a p-value of 0.018, suggesting that fluctuations in network demand significantly precede changes in gas pricing. The reverse direction is not significant, with an F-statistic of 1.26 and a p-value of 0.28, indicating that transaction fees do not predict network activity. The impulse response analysis shows that a one standard deviation shock in Gas Used increases Gas Price for two to three days before returning to equilibrium, while shocks in Gas Price have minimal effects on Gas Used. These findings confirm that Ethereumâs fee market operates primarily as a demand-driven mechanism were congestion and transaction volume shape short-term gas price movements.
Zishan Ashraf Mohammad, Nick Harkiolakis, Saman Sarbazvatan
Although there has been a massive increase in the size and complexity of the cryptocurrency ecosystem, most of the academic research into the relationship between token design parameters and the long-term value of a given token is still very much in its infancy. Most of the research in tokenomics is theoretical in nature, based upon frameworks for understanding, or is focused solely on observing a specific time frame. The authors of this paper address the above mentioned void by studying the statistically significant relationships between five on-chain tokenomic variables--transaction gas fees, total value locked (TVL), token unlocks, tokens burned, and governance concentration (as measured using the Gini coefficient) -- and the market price of Ether (ETH) during a 52 months observation window that began in August 2021 and ended in September 2025. The data for the study consisted of bi-weekly observations (n = 108) which allowed researchers to use three different analytical methods--Spearman correlation analysis, log-linear multiple regression analysis, and an error correction model (ECM) after conducting Johansen cointegration and unit root tests. A cointegrating equation among the variables was established through Johansen Trace Testing, indicating that all of these variables do indeed exhibit a long-run equilibrium relationship. The ECM revealed that the total amount of funds âlockedâ into smart contracts (âtotal value lockedâ) was the strongest single predictor of the price of Ether in both the long run (beta = 0.8, p < 0.001) and short run (beta = 1.18, p < 0.001) specifications. Additionally, it was found that token unlocks have a negative relationship with price (beta = â0.22, p < 0.001). Gas Fees (beta = 0.2, p = 0.021) and tokens burned (beta = 0.15, p = 0.039) had positive coefficients at the 0.01 level in the long-run specification; however, both exhibited extremely high levels of multicolinearity (Variance Inflation Factor>28,000), likely due to their technical/operational linkages under EIP-1559. Voting power did not demonstrate a statistically significant relationship to price (rho =0.143, p > 0.05).
India's asset management systems, especially land records, property registries and ownership documents, face major challenges such as fraud, ownership disputes, slow manual verification and fragmented documentation. These issues affect citizens, government departments, financial institutions and real-estate stakeholders. Blockchain technology provides an opportunity to improve asset management by creating tamper-resistant records, transparent transaction history and automated workflows through smart contracts. This paper studies the use of blockchain platforms for asset management in India with a comparative focus on Hyperledger Fabric and Ethereum. The study analyzes technical architecture, performance, privacy, cost, scalability and implementation barriers. It also considers Indian use cases such as Telangana land parcel initiatives, Karnataka Bhoomi-related digital land record modernization and national-level blockchain adoption efforts. The findings show that Hyperledger Fabric is more suitable for regulated government asset systems because it provides permissioned access, privacy channels, higher transaction throughput and lower operational cost. Ethereum is useful for public transparency and open applications, but its public-chain gas cost, lower throughput and regulatory challenges reduce suitability for high-volume government asset records. The paper concludes that a permissioned blockchain model with proper standards, legacy-system integration, legal recognition and rural digital infrastructure can support scalable blockchain-based asset management in India.
A decentralized ecosystem can capture value and still fail to fund the actors who keep it running. Users may pay fees, tokens may appreciate, issuers may earn revenue, and protocols may burn value, but none of these facts by itself shows that authors, miners, validators, suppliers, storage providers, or other critical participants are actually compensated. This paper argues that traditional value-capture analysis often assumes a centralized pool: once value is captured, it can be reallocated through budgets, contracts, payroll, or managerial discretion. Decentralized ecosystems do not have this default pool. They require routed closure: captured value must pass through a verifiable route to a specified critical incentive recipient, and it must be sufficient relative to that recipient's reward requirement. We formalize this distinction through Route-Admissible Value and operationalize it with the External Value Routing Closure protocol. A contrast set including YouTube, Steem/Steemit, Bitcoin, Ethereum, Aave, Filecoin, USDC, and XRP shows why revenue, fees, burns, token prices, or market capitalization should not be mistaken for sustainable incentive funding.
Vladimir KovĆĄca, Zrinka LackoviÄ Vincek, Suzana KegleviÄ Kozjak
Prior research on cryptoasset valuation has largely adapted discounted cash flow (DCF) models by treating staking rewards and transaction fees as productive cash flows, while insufficiently accounting for monetary characteristics and strategic flexibility inherent to decentralized platforms. This study investigates whether such cashflow based approaches systematically undervalue Ethereum. The central hypothesis is that Ethereumâs intrinsic value cannot be adequately explained by DCF valuation alone, and that monetary premium and technological optionality constitute economically significant components of value. To examine this hypothesis, a multi-layer valuation framework is applied using network and market data from 2022â2025, combining a DCF model, a monetary premium benchmarked against gold based on relative scarcity and adoption, and a real option uplift reflecting future expansion potential. Monte Carlo simulation is employed to test the robustness of the results. The findings indicate that while DCF-based valuations remain relatively stable, total intrinsic value is highly sensitive to assumptions regarding monetary adoption and strategic optionality. These results underline the importance of layered valuation frameworks for decentralized platforms.
This study investigates the predictive performance of decomposition-based deep learning models through a focused case study on Ethereum price forecasting. Using hourly Ethereum price data from 5 September 2020 to 13 July 2025, we develop hybrid forecasting frameworks that integrate three signal decomposition techniquesâWavelet Decomposition (WD), Variational Mode Decomposition (VMD), and Empirical Mode Decomposition (EMD)âwith a Long Short-Term Memory network enhanced by an attention mechanism (LSTMâAttention). The decomposition methods are first applied to extract multiple frequency components from the original time series, allowing the forecasting model to capture both short-term fluctuations and long-term dynamics inherent in this specific digital asset. Each decomposed component is then modeled using the LSTMâAttention architecture, and the forecasts are aggregated to produce the final prediction. The predictive performance of the proposed models is evaluated using MAE, MSE, RMSE, and MAPE, and the results are compared with benchmark models including ARIMA-GARCH and standard LSTMâAttention. Forecast accuracy is assessed through out-of-sample one-step-ahead predictions, and robustness is ensured by averaging results across 10 independent runs. The empirical results demonstrate that incorporating decomposition techniques substantially improves forecasting accuracy. Among the tested models, the EMDâLSTMâAttention framework achieves the best performance, producing the lowest forecasting errors. While focused on the Ethereum market, these findings highlight the effectiveness of combining signal decomposition and attention-based deep learning architectures to enhance predictive performance in high-volatility cryptocurrency environments.
Crowdfunding platforms have democratized access to capital for creators and startups, yet centralized models face high fees, limited transparency, fraud risks, and inter-mediary control over funds. Blockchain addresses these issues through decentralized ledgers, smart contracts, and tokenomics, enabling trustless and transparent funding. This survey exam-ines blockchain-based crowdfunding systems, focusing on DAO implementations, auction mechanisms, social voting models, and milestone-based fund releases on Ethereum and IPFS. We review key works such as VCG auctions, social DAOs like LikeStarter, and the CrowdChain prototype with automated refunds, along with a comparative analysis of voting-, auction-, and token-staked models. Research gaps include scalability, oracle dependencies, Sybil resistance, and regulatory challenges. We propose an enhanced CrowdChain++ system with community verification, multi-milestone campaigns, governance tokens, and Layer-2 optimiza-tion, implemented using Solidity, Hardhat, React, and IPFS. Simulation results show improved automation, reduced gas costs, and stronger fraud resistance, with future directions including ZK-proofs, cross-chain interoperability, and AI-driven anomaly detection.
Sybil attacks remain a primary challenge for Proof-of-Stake (PoS) blockchain systems, as low-cost identity creation can distort validator participation and limit consensus reliability. This study proposes a hybrid participationâgovernance framework that integrates Attribute-Based Access Control (ABAC) and Reputation-Based Access Control (RpBAC) with a trust-based PoS workflow to reduce the influence of suspicious identities during validator selection and block validation. The proposed framework also incorporates graylisting and dynamic rewardâpenalty updates to support adaptive participation control. The strategy was evaluated in a simulation environment informed by Ethereum-derived block metadata, using network sizes ranging from 100 to 1000 nodes and Sybil attack ratios of 30%, 40%, and 50%. Its performance was compared with PoS-only and PoS + ABAC baselines using both security and performance indicators. The results show that the full ABAC + RpBAC configuration achieved the strongest and most stable security performance across the evaluated settings while introducing additional overhead at larger network sizes. These findings suggest that combining policy-based eligibility control with behavior-based reputation control strengthens the resilience against Sybil in PoS-like blockchain environments. However, this improvement requires a measurable trade-off between security and performance.
Aleksei Adadurov, Sergey Barseghyan, Anton Chtepine, Antero Eloranta · 6 authors
Ethereum block builders run sealed auctions among searchers, but nothing in the protocol forces a builder to honor the auction outcome after observing submitted bundles. This paper studies the commitment problem. We model a builder who defects with probability $\varepsilon$ and, upon defection, replicates a type-specific fraction $Îł(Ï)$ of the winning MEV opportunity. Searchers anticipate this behavior and choose between a risky first-price bid and a safe deterrence bid that makes frontrunning unprofitable. The resulting equilibrium is piecewise, with the cost of imperfect commitment depending jointly on replicability and competition. Using the libMEV dataset, we estimate $Îł(Ï)$ from right-tail bribe plateaus and decompose observed auction revenue against the surplus a defecting builder could capture. The results show sharp heterogeneity across MEV types: sandwich opportunities are already highly competitive, while naked arbitrage and liquidations leave substantially more surplus exposed to builder defection. Credible MEV auctions, therefore, require not only an auction format, but also constraints on the builder's ability to use observed bid and payload information ex post.