Tim K. Mackey, Alec J. Calac, Tiana McMann, Ken Miyachi · 10 authors
Background: Historic and ongoing problematic practices regarding the collection, storage, and use of Indigenous health data have led to the need to ensure principles of Indigenous Data Sovereignty (IDS) are followed in research practices and technology development. Objective: This project, a partnership between UC San Diego and the Native BioData Consortium (NativeBio), sought to explore the practical application of blockchain technology and its potential to facilitate Indigenous-led research collaboration. Methods: This project first undertook purposeful relationship building with NativeBio to form a Community Advisory Board (CAB) for identifying community and technology needs for a blockchain research collaboration platform with an initial focus on genomic data. Over a 2-year project period, a series of public meetings and presentations at Indigenous-led conferences introduced the concept of exploring compatibility between blockchain and IDS principles, followed by iterative prototyping and co-design of a blockchain platform with NativeBio, using Ethereum as the underlying protocol. Results: Direct engagement with NativeBio and the CAB informed the initial design and development of a "b-IDS" proof-of-concept (POC) blockchain platform. The POC consists of three main components: (1) the web front-end layer, (2) the Ethereum network that executes the smart contract and blockchain storage aspects of the framework, and (3) the back-end database that stores off-chain interactions and data for future use with external genomic data repositories. After refinement of the POC, a community-based participatory research (CBPR) use case aligned with IDS principles was identified as a practical workflow and incorporated into the design of the POC for implementation. Conclusions: The findings from this project demonstrated the potential use of operationalizing IDS through blockchain technology with proactive and sustained engagement with Indigenous partners. Blockchain technology may have certain advantages over other data governance approaches and systems, facilitating timely oversight, shared decision-making and consent structures, and direct involvement of Indigenous communities in technology design, respecting the core principles of IDS and CBPR. Future development of the blockchain-IDS POC will need to incorporate other research practices and ethics frameworks to expand its use to other public health and biomedical research use cases.
Industrial supply chains involve multiple stakeholders, complex logistics operations, and financial transactions that require transparency, traceability, and secure coordination.Traditional supply chain systems suffer from limited transparency, the risk of data manipulation, and insufficient trust among participants.To address these challenges, this paper proposes a decentralized industrial supply chain management system implemented on an Ethereum-compatible blockchain network.The proposed architecture integrates smart contracts to automate workflows, including stakeholder registration and verification, multi-item order processing, shipment tracking, simulated delivery verification (SDV), and escrow-based conditional payment settlement.The system adopts a hybrid on-chain/off-chain storage architecture in which transactional records are maintained on-chain, while raw material and product images are stored off-chain using the InterPlanetary File System (IPFS).This design reduces blockchain storage overhead while preserving data integrity through cryptographic hash references.To improve operational efficiency and reduce overhead from repeated transactions, the proposed system supports multi-item batch transactions during procurement and ordering, while the logistics and settlement stages maintain per-item execution to preserve traceability and accountability.Experimental evaluation was conducted on the Celo Sepolia network to measure gas consumption and transaction fees for both batch-based and functionally equivalent per-item execution workflows under controlled conditions.The evaluation included multiple predefined workload configurations, and statistical analysis using mean and standard deviation was performed to assess execution stability.The results indicate that transaction aggregation reduces gas consumption by approximately 40-43% for raw material order creation and by 40-48% for raw material operations (addToMultipleCart).Product aggregation workflows also demonstrated measurable gas-efficiency improvements.These findings demonstrate the efficiency benefits of multi-item transaction aggregation within the proposed implementation while preserving lifecycle traceability and escrow-enabled settlement correctness.The reported results represent controlled implementation-level efficiency measurements within the proposed blockchain-based supply chain architecture.
Decentralized manufacturing faces a pre-contractual impasse: a Provider cannot price a service accurately without inspecting the design file, yet the Consumer cannot share that file without exposing intellectual property. We introduce the Optimistic Verifiable Claim (OVC), a blockchain protocol that lets a Consumer publish a verifiable claim about a concealed design (such as the material it consumes) and a Provider price and bid on it without seeing the design. The claim is committed when the service is posted and stands unless the selected Provider challenges it; a challenge triggers a deterministic on-chain check that exposes any dishonesty, and the design is disclosed only to settle a dispute, never on the honest path. We implement four checks (authorized key access, delivery-channel integrity, syntactic conformance, and declared material consumption) in Solidity and measure them on a real 6.41 MB G-code file, the 3DBenchy, across Ethereum, Arbitrum, and opBNB. Every service incurs the cost of posting the encrypted design, with or without a dispute. For the 3DBenchy, the no-dispute outcome costs \$7,207 in up to 9 hours on Ethereum, \$288 in 3 min on Arbitrum, and \$2.87 in 2 min on opBNB, and a fully contested dispute costs \$49,660 in up to 57 hours on Ethereum, \$1,988 in 19 min on Arbitrum, and \$19.73 in 13 min on opBNB. Costs and times grow with size: for a 50 MB industrial design, an undisputed service reaches \$56,173 and up to 3 days on Ethereum against \$22.36 and 16 min on opBNB, and a fully contested dispute reaches \$488,440 over up to 18 days on Ethereum against \$195 and 1.6 hours on opBNB. Of the four, the material-consumption check is the costliest, its predicate being the most expensive to evaluate on-chain. OVC makes confidential, claim-based bidding economically feasible on Arbitrum and opBNB, but not on Ethereum at industrial scale.
Vabuk Pahari, B. Chandrasekaran, Johnnatan Messias, Krishna P. Gummadi · 5 authors
A decentralized autonomous organization (DAO) is a governance entity that allows its stakeholders to manage blockchain-based protocols through smart contracts. The DAO explicitly specifies how stakeholders make and enforce decisions concerning a protocol's operation in a smart contract, aptly referred to as its governance contract. The design of this governance contract, therefore, has far-reaching implications for the security (trust) and privacy (transparency) of the smart contracts managed by the DAO and its stakeholders. In this work, we (i) explicate the trust and transparency trade-offs of the design choices in implementing a DAO and (ii) highlight how poor choices introduce critical vulnerabilities, using real-world examples as case studies. To this end, we analyze $48$ public, actively used Ethereum-based DAOs that control a vast capital. We classify the design choices into a handful of key dimensions that succinctly capture how a DAO's stakeholders initiate a protocol change, vote on it, and, based on the voting outcome, execute that change. Our analyses crucially uncover a new class of attacks, which we call governance attacks, that directly exploit the fundamental design of a DAO's governance mechanisms, even if we assume bug-free implementations.
Blockchains rely on transparency and immutability to ensure trust, but these guarantees come at the cost of an ever-growing ledger that increasingly threatens decentralization by making it more expensive to store and maintain the full transaction history. In this work, we introduce state-invariant transactions, defined as transactions whose inclusion or removal does not affect the resulting blockchain state beyond transaction fees. We argue that these transactions constitute a form of on-chain spam because they consume execution, bandwidth, storage, and blockspace without contributing to the final ledger state. We present the first large-scale measurement of state-invariant transactions across Ethereum, Optimism, and Base, identifying nearly 1.4 billion such transactions. While only 2.6% of Ethereum transactions are state-invariant, they account for 24% of transactions on Optimism and 37% on Base, representing a significant source of unnecessary resource consumption on Layer-2 blockchains. We show that speculative Maximal Extractable Value (MEV) is the dominant source of state-invariant transactions on Optimism and Base, accounting for 57% and 68%, respectively, but is not the only source as previously assumed. Moreover, despite its popularity, speculative MEV is not the most profitable strategy once the costs of state-invariant transactions are considered. Beyond MEV, we identify substantial malicious activity, with address poisoning campaigns accounting for 53% of non-reverted state-invariant transactions on Ethereum. Our findings suggest that mitigating state-invariant transactions could substantially reduce blockchain resource consumption and transaction costs while limiting phishing campaigns and other forms of blockchain abuse.
Abstract A secure platform for exchanging and storing agricultural data is provided via a blockchain-powered framework. By integrating edge computing, blockchain technology, and the Internet of Things (IoT) the production of crops can be boosted while using fewer natural resources. In the sector of agriculture, sensors and equipment gather various data about the landscape, which can subsequently be delivered to a server in a cloud environment. Due to its extreme fragility, these data must be securely stored and guarded from unwanted access. The core aim of this work is to propose a hybrid Reconditioned Random value-based Wombat Optimization with Adaptive Multi-scale Vision Transformer-based EfficientNet (RRWO-AMViT-ENet) model integrated with Ethereum smart contracts for secure pest detection and smart irrigation in IoT environments. The gathered agricultural images are initially stored and managed using the Ethereum blockchain. Then, node authentication is performed using the Smart Contract-based Adaptive Deep Support Vector Machine (SC-ADSVM). A Reconditioned Random value-based Wombat Optimization (RRWO) is utilized to optimize the variables of the developed SC-ADSVM. In order to perform pest detection and smart irrigation, the Adaptive Multi-scale Vision Transformer-based EfficientNet (AMViT-ENet) is used. The proposed model is implemented on the IP102-Dataset, where it obtained an accuracy of 96.39% in the pest detection operation. Thus, the proposed model provides effective results for pest detection and the smart irrigation process. From the attained results, it is concluded that the recommended strategy can provide intelligent service to the farmer.
Blockchain technology provides a decentralized and secure data-management system. However, PoW consensus remains vulnerable to the risk of 51% attacks, where a single miner or pool controls over half of the network and can manipulate blocks and execute double-spending. This paper proposes a defense mechanism called Consensus-Level Restriction (CLR) for Ethereum Classic. It limits the number of sequential blocks from a single miner to reduce the feasibility of 51% attacks. We evaluated the effectiveness of CLR by using BlockSim simulator. Results show that CLR reduces the success rate of 51% attacks while maintaining a decentralized structure. This work strengthens blockchain security without compromising PoW principles.
Abstract: Product traceability within supply chains is a critical concern that has gained increasing attention in recent years. One promising solution to this problem is the use of Public Blockchain (PBC) technology, which offers an immutable, transparent, and decentralized ledger for storing key information such as ownership transfers and distribution records. These blockchain-based systems significantly enhance traceability by ensuring that data, once recorded, cannot be altered. However, a key challenge arises from the fact that information stored on public blockchains is freely accessible to anyone, potentially exposing sensitive distribution data. In this paper, I introduce a novel method that preserves the privacy of distribution data while maintaining high traceability in supply chain systems leveraging PBC. The proposed method utilizes encryption to safeguard sensitive data and Zero-Knowledge Proofs (ZKPs) to allow supply chain participants to authenticate themselves without revealing private information, such as their blockchain addresses. The solution was implemented using Ethereum smart contracts and evaluated for its cost-effectiveness. The results show that the transaction fee per supply chain participant is capped at USD 2.6, demonstrating that the approach is practical for real-world deployment.
Reshma D’Souza, S Sheela, H S Sameena, S Jyothi · 6 authors
Abstract: In this era where technology is used to create unidentical counterfeit products, Finding the original objects is a very tedious task for the users. These Counterfeit Products affect the health of the user in the case of medical and skin care products also. This project implements Blockchain, a new Technology which is used to overcome this problem. Blockchain technology is the distributed, and immutable technology that provides data consistency and security. Here a QR code is generated for each product that is linked to the database which in turn is mapped to the chain nodes. By scanning this QR code the user can detect the original products amongst the fake ones. It highlights the need of cryptocurrency in the broader vision of supply chain security, elaborating on how Blockchain network, particularly using Ethereum Framework, provides a decentralized and transparent ledger for tracking and validating products.
In Ethereum, transaction inclusion is rarely in question; what matters is the delay until inclusion. Currently, block builders could exercise censorship across consecutive blocks, threatening time-critical applications, such as on-chain auctions. To mitigate this risk, existing proposals such as FOCIL, scheduled for deployment in late 2026, assign a committee to list transactions for mandatory inclusion. However, no committee member is held accountable for the actual inclusion of the transactions: an adversary can bribe the entire committee to omit any transaction for less than 2 Euro per block under current conditions. We argue that accountability, i.e., requiring all exclusion decisions to be publicly disclosed and verifiably complete, with violations attributable to a specific party, substantially raises censorship costs. To this end, we propose Fair Forward Inclusion Lists (FairFIL) as an accountable censorship resistance mechanism for Ethereum. In FairFIL, every builder must publish all transactions the builder chooses to censor, subject to a protocol-anchored policy; a committee verifies the completeness and validity of this disclosure. The subsequent builder must include these transactions, forfeiting the full block reward upon any omission. Therefore, under FairFIL, extending censorship beyond a single slot requires an assembler to forfeit a full block reward. We show that compliance is rational for all participants within our behavior model. Our empirical evaluation on Ethereum mainnet indicates that multi-block censorship costs one order of magnitude more than under existing proposals, while leaving the builder's MEV extraction freedom largely intact. Initial measurements further suggest that the mempool consistency FairFIL requires is met in practice.
Direct user-specified research topic: Autonomous agent wallets spend under English mandates like 'only stablecoin swaps under $200 daily, never bridge, never touch unaudited pools', yet deployed policy engines (Safe Transaction Guards, ERC-7579 modules, session-key allowlists) enforce only stateless numeric and selector limits and cannot express 'unaudited' or 'per day', while a naive base-model prompt over raw hex calldata cannot recover function, recipient or token flow and confabulates verdicts. Evaluate a tool-augmented structured-decoding LLM judge that fetches ABIs from Sourcify and Etherscan, decodes calldata including multicall and Permit2 payloads, simulates via eth_call state overrides for token-flow and approval deltas, attaches counterparty features (contract age, verification), and emits constrained JSON: in_policy, violated_clause quoted verbatim, offending_calldata_field. Read Ethereum and Base: ERC-20 Transfer/Approval logs, Uniswap/1inch routers, Across/Stargate bridges, Permit2 at 0x000000000022D473030F116dDEE9F6B43aC78BA3. Measure macro-F1 and clause-attribution precision on 600 hand-labeled mandate/transaction pairs plus replay accuracy on transactions whose approvals owners later revoked, beating a naive raw-hex prompt and a Safe Guard numeric-allowlist baseline. Deliver as the prototype a minimal runnable Python MCP server (stdio) exposing the priced AI tool screen_transaction_against_mandate that invokes a language or ML model over onchain data to produce its output, with a typed input/output schema, an x402-style pay-per-call metering stub that records a per-call price in USDT and emits a settlement receipt, and one smoke test that exercises the tool end to end.. Investigate this topic end-to-end: survey the state of the art, identify a concrete tractable research question within it, design and run an experiment, and report results.
Digital assets, a broad term encompassing crypto-currencies, tokens and digital representations of value, have transformed the financial landscape over the past decade. Ghana has transitioned from an unregulated crypto-currency environment to a structured, licensed digital assets space following the passage of the Virtual Asset Service Providers (VASP) Act 2025 Act 1154. Unlike traditional assets, digital assets exist exclusively in electronic form and are secured through cryptographic techniques, most notably blockchain technology. Bitcoin, Ethereum, and other crypto-currencies serve as prominent examples, alongside digital tokens used in decentralized finance (DeFi), security tokens, and stablecoins. They may serve a variety of functions, including use as a medium of exchange, for investment, or as a means of accessing goods, services, or applications within specific ecosystems. These assets include crypto-currencies, tokens, stablecoins, and other blockchain-based instruments. Global digital assets represent any item of value securely stored and managed via distributed ledger or blockchain technology. Encompassing cryptocurrencies, stablecoins, tokenized securities, and non-fungible tokens (NFTs), the sector has rapidly expanded into mainstream finance, revolutionizing global payments, portfolio diversification, and record-keeping. This article discusses the challenges and opportunities of digital currencies and the way forward. This research shows that digital currencies have advantages like making transactions faster, cheaper, and more accessible and also reveals a lot of disadvantages like creating major risks concerning compliance with regulations, cybersecurity, and potential impacts on monetary policy. The review emphasizes the necessity for robust regulatory frameworks for digital assets. It supports both innovation and stability for the digital currencies. It suggests that policymakers and financial institutions should adapt to changes and face the challenges by integrating digital currencies with existing systems. Overall, this review highlights the potential of digital currencies to transform finance. It also stresses the importance of focusing on the challenges they pose to ensure they can coexist successfully with traditional financial systems. As digital currencies evolve, the Ghanaian traditional financial sector faces pressure to adapt, with CBDCs, in particular, being explored as a secure, regulated alternative to volatile crypto-assets. nThe findings revealed that the central bank must adopt robust regulatory and licensing frameworks must align with Virtual Assets Service Providers (VASP) (Act 2025 Act 1154) by enforcing strict licensing for exchanges and custodians while adhering to AML/CFT (Anti-Money Laundering) directives. Also, the Bank of Ghana and the Securities and Exchange Commission must develop a comprehensive public education programme on the digital assets in the financial ecosystem. Given the novelty of the trend of criminality in the digital asset space, the establishment of specialized cybercrime courts to be presided over by judges, proficient in digital law and cybercrime would be of immense benefit. The mandate of such courts could be to expedite trials and ensure thorough adjudication of complex cyber cases. This would have the combined effect of empowering the Ghana Police Service and Cyber-Security Authority to fully invest time, money, and human resources towards the investigation of cybercrime, as well as serve as a deterrent for criminal elements, ultimately protecting our citizens and providing justice for those seeking redress.
Ethereum non-fungible tokens (NFTs) implement ownership, transfer, authorization, and metadata operations through smart contracts, making contract vulnerabilities a direct risk to digital assets. Existing static analyzers provide efficient rule-based screening but can struggle with application-specific logic, whereas unconstrained large language model analysis may be distracted by irrelevant code or produce inconsistent outputs. We present a vulnerability-detection method that combines vulnerability-focused code slicing, an ERC-721-oriented knowledge base, and constrained DeepSeek analysis. Regular-expression patterns locate candidate statements for reentrancy, integer overflow or underflow, and timestamp dependence. A structure-aware context-window algorithm then extracts line-numbered code slices. DeepSeek analyzes each slice using explicit decision rules and a fixed output schema, and the resulting records support automated batch processing. On 450 NFT contract samples, the full configuration produced 437 positive labels, corresponding to a reported positive-label rate of 97.1%. Removing the external knowledge base reduced this rate to 87.11%, while analyzing complete contracts without the knowledge base reduced it to 73.78%. These results indicate that focused code context and domain constraints materially affect the detector's reported output.
Fei Wu, Thomas Thiery, Stefanos Leonardos, Carmine Ventre
Block production in modern blockchains is increasingly shaped by economic gains that arise from control over transaction ordering. These gains—known as Maximal Extractable Value (MEV)—have led to concerns about centralization and market power among blockchain consensus participants. To address these concerns, Ethereum introduced Proposer-Builder Separation (PBS), in which specialized block builders compete in block building auctions to construct blocks on behalf of validators. The current implementation of PBS, MEV-Boost, mediates this competition through an open-bid first-price ascending auction, termed the MEV-Boost auction. This paper analyzes the strategic incentives of builders in MEV-Boost auctions. We develop an agent-based simulation framework and apply empirical game-theoretic analysis to study how asymmetries in network latency and access to MEV opportunities shape bidding behavior and market concentration. Our findings show that while latency differences mildly affect builder incentives, MEV opportunity access fundamentally alters equilibrium strategies: builders with privileged access to MEV opportunities bid less aggressively, maintain higher profit margins, dominate market share, and reduce proposer revenue. These effects contribute to centralization and oligopolistic outcomes in the builder market. To validate these findings, we further analyze an idealized symmetric benchmark market where builders have comparable latency and MEV access. Under such settings, the auction behaves as expected—bidding is competitive, proposer revenue is higher, and the market is more decentralized—confirming that the observed inefficiencies arise specifically from the asymmetries present in practice.
We provide a large-scale empirical audit of DEX routing using 2.98 million WETH-USDC swaps on Ethereum. Comparing realized routes with optimized benchmarks, we measure an average shortfall of 2.02 bps per trade or \$24 million. To attribute losses, we introduce three reproducible optimal benchmarks: a Support-Constrained Optimum (SCO) that evaluates split quality conditional on the pools actually used; a Full-Venue Optimum (FVO) that considers all available pools to quantify the value of broader pool access; and a Gas-Aware FVO (G-FVO) that augments FVO with gas costs to capture the trade-off between additional pool usage and gas expenditure. Computing these benchmarks at scale is enabled by a bisection-based algorithm for optimal routing across multiple pools for the same token pair. Two regularities emerge. First, information timeliness is crucial: moving from execution-time state to one-block lagged state optimization significantly raises mean shortfall and additional delays further degrade performance, albeit with diminishing increments; evaluated on the same stale snapshots, realized routes lie closer to optimal, indicating timing-mismatch as a key component. Second, inefficiency is heterogeneous and heavy-tailed: small trades suffer higher percentage losses, while a few extreme outliers dominate the aggregate dollar shortfalls. Finally, we demonstrate that sandwiching attacks drive a significant fraction of routing sub-optimality. Our benchmark protocol and algorithm offer a rigorous, reproducible basis for evaluating and improving information-timely, gas-aware routing.
Zero-Knowledge Ethereum Virtual Machines (zkEVMs) secure Ethereum rollups by generating zero-knowledge proofs that guarantee off-chain execution correctness. However, subtle implementation bugs (e.g., incorrect gas accounting) can lead to valid proofs certifying semantically faulty states, thereby silently defeating cryptographic guarantees. Formal verification via SMT solvers can prevent this, but is bottlenecked by specification: current zkEVM development practice lacks automated methods to translate Rust opcode handlers into verification models. Current practices rely on unsustainable manual specifications, while LLM-based approaches suffer from hallucination and lack formal guarantees. To address this, we propose VeriSynth, a framework that synthesizes executable Python/Z3 verification models from Rust zkEVM code. VeriSynth enforces a hybrid paradigm: an LLM acts strictly as a formalization frontend to translate code into symbolic constraints, while an SMT solver serves as the correctness arbiter. To handle complex multi-component state transitions, VeriSynth integrates semantic decomposition, retrieval-grounded prompting, and verification-guided auto-repair into a closed-loop pipeline. We evaluate VeriSynth on the first source-level zkEVM verification benchmark, encompassing both correct and faulty opcode implementations. VeriSynth achieves a bug detection rate of over 90%, substantially outperforming direct and conversational LLM baselines, as well as a production-grade handwritten mutation-testing suite. Ablation studies confirm that each pipeline component is critical to the framework's overall effectiveness.
Gouher Ahmed, Hamza Naim, Aqila Rafiuddin, Mohammed Nizamuddin · 5 authors
This study deals with the performance analysis and volatility estimation of conventional indices including Dow Jones, S&P 500, Brent Oil, Crude Oil and Gold and cryptocurrencies including Bitcoin and Ethereum for the period January 3, 2011 to November 26, 2021 for all of the indices except Ethereum for which the period chosen was from March 10, 2016 to November 26, 2021 due to late incorporation of the cryptocurrency. The stationarity, heteroscedasticity, and serial correlation of the data were considered. Time series regression using the GARCH model is applied for performance analysis and volatility estimation. GARCH (1, 1) estimates show the high performance of cryptocurrencies over the conventional indices, except Gold, which was insignificant, with Ethereum followed by Bitcoin being the most volatile among the different indices. However, Gold remains inert in response to the different indices. However, although the cryptocurrencies add to the country’s revenue, thus minimizing the deficits, there should still be proactive policies and practices to prevent the exploitation of stakeholders, especially for the sake of minority ones.
This dataset accompanies the paper An Architectural and Empirical Study of Root-Only Zero-Knowledge Verification and contains the scripts, intermediate artifacts, and published results used to reproduce the empirical evaluation. The repository is organized around two experiment groups: Blockchain-Side Deployment and Verification: deployment and Groth16 proof verification on Ethereum Sepolia and zkSync Sepolia, including contract sources, Merkle-tree inputs, Groth16 proofs, and blockchain measurement CSVs and figures. ZKP proving and off-chain verification: Constraint-count comparison — Groth16 R1CS constraint counts and expanded PLONK gate counts for Merkle-tree depths 5–15, with measurement scripts and summary CSVs/figures. Proving-time comparison — off-chain Groth16 and PLONK proving benchmarks across depths 5–15, including proving scripts, generated witness/proof/key artifacts, and benchmark CSVs/figures.
This dataset accompanies the paper An Architectural and Empirical Study of Root-Only Zero-Knowledge Verification and contains the scripts, intermediate artifacts, and published results used to reproduce the empirical evaluation. The repository is organized around three experiment groups: On-chain verification — deployment and Groth16 proof verification on Ethereum Sepolia and zkSync Sepolia, including contract sources, Merkle-tree inputs, Groth16 proofs, and blockchain measurement CSVs and figures. Constraint-count comparison — Groth16 R1CS constraint counts and expanded PLONK gate counts for Merkle-tree depths 5–15, with measurement scripts and summary CSVs/figures. Proving-time comparison — off-chain Groth16 and PLONK proving benchmarks across depths 5–15, including proving scripts, generated witness/proof/key artifacts, and benchmark CSVs/figures. Shared setup files include Circom circuits, Merkle-tree preparation scripts, circuit inputs, and compiled circuit artifacts. Most of the generated data is produced by the provided scripts and does not need to be included separately if the reproduction pipeline is documented.
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Formal Methods in Verification
Physical Unclonable Functions (PUFs) and Hardware Security
Federated Learning (FL) enables collaborative model training across decentralized participants without sharing raw data. However, existing FL systems remain vulnerable to Byzantine attacks and suffer from a lack of accountability, verifiability, and economic incentives for honest participation. We present BFL-Guard, a novel blockchain-orchestrated federated learning framework integrating: (i) zk-SNARK-based zero-knowledge gradient proofs, (ii) an on-chain Byzantine-tolerant aggregation smart contract, and (iii) a tokenized incentive protocol (FedToken). BFL-Guard stores model checkpoints as IPFS hashes anchored on Ethereum, ensuring tamper-evident auditability. Experiments on CIFAR-10 and Shakespeare benchmarks demonstrate 95.2% and 87.6% accuracy in IID and Non-IID settings, surpassing all baselines while converging 12.4% faster even under 30% Byzantine injection.
Similar to all other cryptocurrency platforms, Ethereum is constantly confronted with malicious activities. In recent years, research efforts have targeted the detection and mitigation of malicious activities and the associated accounts within the Ethereum ecosystem. Yet, the malicious accounts represent only a small visible part of the substantial collaborative network enabling these activities. In this work, we offer the first analysis of this collaborative network and the corresponding affiliate accounts that often remain hidden from detection. We present enEtherShield, an enhanced framework for detecting affiliate accounts that assist malicious accounts in the related Ethereum scams. Our research findings lay the foundation for the detection of the collaborative network enabling Ethereum scams.
Aditya Saraf, Ioannis Kaklamanis, Sarisht Wadhwa, Fatima Elsheimy
Censorship resistance is the defining advantage of blockchains over their centralized counterparts. Yet block proposers censor transactions for many reasons, from legal consequences to economic incentives. We study economically-incentivized censorship, modeled by an adversary who bribes proposers to exclude a target transaction, and define the economic censorship resistance (eCR) of a transaction as the adversary's expected cost of successful censorship divided by the user's expected payment for inclusion. Single-proposer systems are structurally weak by this measure: under a first-price auction the adversary need only match the user's bid, and fee burning pushes eCR to a few percent of what the user pays. We therefore turn to multiple concurrent proposers (MCP), where block capacity is divided among $n$ proposers and the block is the union of their sub-blocks. While MCP can substantially increase the cost of censorship by requiring the adversary to bribe many proposers, it also introduces transaction duplication, reducing throughput. The resulting trade-off depends critically on the transaction fee mechanism (TFM), which determines how fees are shared among competing proposers. We create a game theoretic model where validators construct blocks from a shared mempool, subject to an adversary's bribery attempt. We provide an algorithm that solves for the mixed equilibrium of a given mempool, which is characterized by the probability of including each transaction. This algorithm works for a wide class of TFMs, and allows us to calculate the expected throughput and censorship resistance for any bid distribution. We then use simulations to show how the eCR and throughput vary as the number of proposers increases. We compare three TFMs, finding that the duplication-penalizing TFM dominates the others across many settings. We also validate our findings with empirical Ethereum data.
In this paper, we develop an open-economy macroeconomic model of a Proof-of-Stake network to analyze nominal token-price dynamics and the systemic effects of speculative capital. We first consider a network populated solely by active utility users, who finance network activity through a steady exogenous inflow of fiat currency. We prove the existence of a unique, globally asymptotically stable steady-state equilibrium with a well-defined nominal token price and derive a closed-form expression for the network's relaxation time. Calibrating the model using parameters representative of the current Ethereum network, we estimate a relaxation half-life of approximately 46 years. This extreme macroeconomic inertia implies that the token price may remain persistently displaced from its evolving steady-state benchmark, producing sustained price overshooting as the network adjusts to changing fundamentals. We then introduce an Investor class to examine the effects of passive and active speculative capital. We show that passive institutional staking compresses the native staking yield and creates a structural imbalance that systematically raises the nominal token price while shifting consensus ownership away from active utility users. Active speculative capital has a qualitatively different effect. In response to capital shocks, the Consumer class's rigid preference for fiat-denominated consumption generates an endogenous constant-value strategy. This mechanism shifts staked-token ownership from the Investor class toward active utility users, with potentially favorable implications for consensus decentralization.
As the blockchain technology and decentralized finance have grown rapidly, the number of fraudulent and anomalous activities has risen.The paper suggests a detectable graphbased anomaly detection system to detect suspicious Ethereum transactions.One 10,000 Ethereum transactions dataset was gathered through the Etherscan API within a 14 hour observation period and a directed transaction graph was created out of that dataset, where 14 behavioral node features were engineered.Three graph neural network (GNN) models, namely, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and GraphSAGE, were checked on 5-fold cross-validation, and compared to three standard baseline classifiers, which are Logistic Regression, Random Forest, and XGBoost.GraphSAGE had the highest overall accuracy of 82.32, F1-score of 0.6389, and ROC-AUC of 0.8202, and GCN and GAT had near-zero recall on the minority class.XGBoost was the best baseline with the highest accuracy (94.41) but with significantly lower recall (0.2766) and F1-score (0.3801) compared to GraphSAGE, which is indicative of graph-based models being more balanced in precision and recall in detecting anomalies with class imbalance.The Local Interpretable Model-agnostic Explanations (LIME) showed outgoing transaction value features and account balance to be most important predictors of anomalous behavior.The results establish the promise of using GNNs in conjunction with explainable AI to secure blockchains, as well as reveal the challenges such as the class imbalance and ground-truth verified labels.