Robiah Arifin, Wan Azelee Wan Abu Bakar, Mustafa Man, Mohamad Afendee Mohamed · 5 authors
The issue of fake certificates has been widely identified, and their prevalence has increased significantly in recent years. This growing trend has become a global concern due to its adverse impact on educational standards. A key factor contributing to the problem is the continued reliance on manual processes for issuing and verifying certificates. To address these challenges, this study proposes the use of an authority round (AuRa) consensus algorithm for managing certificate data on the Ethereum blockchain. AuRa, a member of the proof of authority (PoA) family, facilitates consensus among nodes distributed across multiple servers and networks. This mechanism plays a vital role in preserving the integrity and decentralization of the blockchain while ensuring the security of transactional data. Furthermore, the study investigates how AuRa enables efficient certificate data transactions within a private Ethereum environment. It also evaluates the algorithm's performance in terms of transaction speed per second (TPS) and throughput per second (TGS), demonstrating its effectiveness for managing certificate transactions on a blockchain network. Then the TPS and TGS results substantiate the suitability of AuRa for digital certificate generation, evidenced by its stable and efficient performance within a controlled private server environment.
The Ethereum Virtual Machine (EVM) is a stack-based virtual processor that executes smart contract bytecode sequentially. While this design ensures determinism and correctness, it inherently limits instruction throughput. This paper presents a feasibility study of instruction-level pipelining within the EVM interpreter architecture. By analyzing the internal execution flow of the EVM as implemented in the Go-Ethereum (geth) client, the study identifies the program counter dependency, particularly under jump instructions, as the principal control hazard preventing naïve pipelining. A two-stage pipelined execution model is proposed, separating opcode fetch and decode from execution and program counter update, with a feedback mechanism to preserve EVM semantics. The work focuses on architectural feasibility rather than performance evaluation and optimization, demonstrating that pipelining inside the EVM interpreter is conceptually possible under controlled synchronization. Limitations, design challenges, and future research directions are discussed.
Elvis Konjoh Selabi, Maurizio Murgia, António Ravara, Emilio Tuosto
We propose a formal approach for specifying and implementing decentralised coordination in distributed systems, with a focus on smart contracts. Our model captures dynamic roles, data-driven transitions, and external coordination interfaces, enabling high-level reasoning about decentralised workflows. We implement a toolchain that supports formal model validation, code generation for Solidity (our framework is extendable to other smart contract languages), and automated test synthesis. Although our implementation targets blockchain platforms, the methodology is platform-agnostic and may generalise to other service-oriented and distributed architectures. We demonstrate the expressiveness and practicality of the approach by modelling and realising some coordination patterns in smart contracts.
We document stable cross-asset patterns in cryptocurrency limit-order-book microstructure: the same engineered order book and trade features exhibit remarkably similar predictive importance and SHAP dependence shapes across assets spanning an order of magnitude in market capitalization (BTC, LTC, ETC, ENJ, ROSE). The data covers Binance Futures perpetual contract order books and trades on 1-second frequency starting from January 1st, 2022 up to October 12th, 2025. Using a unified CatBoost modeling pipeline with a direction-aware GMADL objective and time-series cross validation, we show that feature rankings and partial effects are stable across assets despite heterogeneous liquidity and volatility. We connect these SHAP structures to microstructure theory (order flow imbalance, spread, and adverse selection) and validate tradability via a conservative top-of-book taker backtest as well as fixed depth maker backtest. Our primary novelty is a robustness analysis of a major flash crash, where the divergent performance of our taker and maker strategies empirically validates classic microstructure theories of adverse selection and highlights the systemic risks of algorithmic trading. Our results suggest a portable microstructure representation of short-horizon returns and motivate universal feature libraries for crypto markets.
With the rapid advancement of Web 3.0 technologies, public blockchain platforms are witnessing the emergence of novel services designed to enhance user privacy and anonymity. However, the powerful untraceability features inherent in these services inadvertently make them attractive tools for criminals seeking to launder illicit funds. Notably, existing de-anonymization methods face three major challenges when dealing with such transactions: highly homogenized transactional semantics, limited ability to model temporal discontinuities, and insufficient consideration of structural sparsity in account association graphs. To address these, we propose GradWATCH, designed to track anonymous accounts in Ethereum privacy-preserving services. Specifically, we first design a learnable account feature mapping module to extract informative transactional semantics from raw on-chain data. We then incorporate transaction relations into the account association graph to alleviate the adverse effects of structural sparsity. To capture temporal evolution, we further propose an edge-aware sliding-window mechanism that propagates and updates gradients at three granularities. Finally, we identify accounts controlled by the same entity by measuring their embedding distances in the learned representation space. Experimental results show that even under the conditions of unbalanced labels and sparse transactions, GradWATCH still achieves significant performance gains, with relative improvements ranging from 1.62% to 15. 22% in the MRR and from 3. 85% to 7. 31% in the F_1.
Digital identity verification often forces a privacy trade-off, where users must disclose sensitive personal data to prove simple eligibility criteria. As blockchain applications integrate with regulated environments, this over-disclosure creates significant risks of data breaches and surveillance. This work proposes a general Selective Disclosure Framework built on Ethereum, designed to decouple attribute verification from identity revelation. By utilizing client-side zk-SNARKs, the framework enables users to prove specific eligibility predicates without revealing underlying identity documents. We present a case study, ZK-Compliance, which implements a functional Grant, Verify, Revoke lifecycle for age verification. Preliminary results indicate that strict compliance requirements can be satisfied with negligible client-side latency (< 200 ms) while preserving the pseudonymous nature of public blockchains.
Bitcoin's block reward is scheduled to decline to zero, raising concerns about whether the network can remain secure once miners rely solely on transaction fees. This paper seeks to identify the conditions under which large-scale and persistent deviation from honest mining can arise. We analyze and compare the payoffs of honest and deviating miners in a sequential decision model, and identify a deviation threshold $G_t$ at which honest mining ceases to be privately optimal. Around the 2024 Bitcoin halving, we show that current mining behavior does not exhibit large-scale or structural deviation. However, when the block reward is removed, the $G_t$ criterion implies that deviation can arise even with a very small fraction of transaction fees. Finally, we evaluate three protocol-level mechanisms: Base Fee, Fee Floor, and an adaptive maximum block size rule, and show that their combination raises the deviation threshold and mitigates incentive breakdown in a fee-only regime. These results provide a practical benchmark for assessing Bitcoin's security as block rewards disappear.
Maxime Nicolas, Francois Sicard, Marion Laboure, Zixin Sun · 5 authors
This study investigates the transmission of monetary policy narratives to Bitcoin prices, distinguishing the impact of ex-ante expectations from ex-post interest rate implementation. We introduce a high-frequency Monetary Policy Expectations (MPE) index, using a Large Language Model (LLM)-based classification of 118,000+ market messages to achieve a precise hawkish/dovish decomposition. Results from a framework combining Long Short-Term Memory (LSTM) networks with SHapley Additive exPlanations (SHAP) indicate that Bitcoin functions as a sensitive barometer of central bank signaling; specifically, hawkish narratives consistently trigger negative price responses independently of actual Federal Funds Rate adjustments. We demonstrate that the MPE index Granger-causes Bitcoin returns at short-to-medium horizons, establishing linear predictive causality, while the LSTM-SHAP framework reveals pronounced non-linear, macroeconomic regime-dependent interactions. These findings highlight Bitcoin's structural sensitivity to global monetary discourse, establishing LLM-derived sentiment as a potent leading macroeconomic indicator for the digital asset landscape.
This study introduces a novel approach to walk-forward optimization by parameterizing the lengths of training and testing windows. We demonstrate that the performance of a trading strategy using the Exponential Moving Average (EMA) evaluated within a walk-forward procedure based on the Robust Sharpe Ratio is highly dependent on the chosen window size. We investigated the strategy on intraday Bitcoin data at six frequencies (1 minute to 60 minutes) using 81 combinations of walk-forward window lengths (1 day to 28 days) over a 19-month training period. The two best-performing parameter sets from the training data were applied to a 21-month out-of-sample testing period to ensure data independence. The strategy was only executed once during the testing period. To further validate the framework, strategy parameters estimated on Bitcoin were applied to Binance Coin and Ethereum. Our results suggest the robustness of our custom approach. In the training period for Bitcoin, all combinations of walk-forward windows outperformed a Buy-and-Hold strategy. During the testing period, the strategy performed similarly to Buy-and-Hold but with lower drawdown and a higher Information Ratio. Similar results were observed for Binance Coin and Ethereum. The real strength was demonstrated when a portfolio combining Buy-and-Hold with our strategies outperformed all individual strategies and Buy-and-Hold alone, achieving the highest overall performance and a 50 percent reduction in drawdown. A conservative fee of 0.1 percent per transaction was included in all calculations. A cost sensitivity analysis was performed as a sanity check, revealing that the strategy's break-even point was around 0.4 percent per transaction. This research highlights the importance of optimizing walk-forward window lengths and emphasizing the value of single-time out-of-sample testing for reliable strategy evaluation.
Pavel Ciaian, d’Artis Kancs, Miroslava Rajcaniova
Abstract Around three-quarters of Bitcoin transactions occur off-chain. While most empirical studies focus exclusively on on-chain transactions, only few papers analyse off-chain transactions. The empirical evidence of Bitcoin market considering both types of trading strategies remains limited. This paper is one of the first to present an empirical analysis of both on- and off-chain demand and supply-side factors and their short- and long-run relationship with the Bitcoin price. Employing the ARDL approach with daily data from 2019 to 2024, we demonstrate a differentiated contribution of on-chain and off-chain drivers to the Bitcoin price. In the long-run, off-chain demand pressures have a significant relationship with the Bitcoin price. In the short-run, both off-chain demand and supply factors are statistically significantly related to the Bitcoin price. The relationship between blockchain transactions and the Bitcoin price is also present, albeit likely operating through a different channel than off-chain trades. These findings confirm the dual nature of the Bitcoin market, in which price movements are related to both market fundamentals and speculative considerations captured by on- and off-chain trades, respectively.
Elizaveta Pertseva, Valentin Robert, Clark Barrett, James Parker
Efforts to verify Zero-Knowledge Proof circuit encodings have highlighted the challenge of proving the correctness of quantifier-free statements that make use of both bitvector and finite field operations. Existing verification workflows are either manual or rely on SMT solvers, which scale poorly on some classes of problems for reasons that include difficulties with conversion operators and challenges reasoning about inequalities. To address these limitations, we present a novel Lean tactic BitModEq that leverages range lemmas and case analysis to produce verified translations from finite fields to bitvectors. Our approach, combined with bit-blasting, outperforms state-of-the-art SMT solvers, solving 19% more ZKP arithmetization benchmarks.
Open access
4 source records
cs.LO
Formal Methods in Verification
Physical Unclonable Functions (PUFs) and Hardware Security
Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-off between convenience and privacy by routing sensitive financial data through centralized servers, increasing security risks, and diminishing user trust. This study introduces a device-native autonomous Artificial Intelligence (AI) agent system for privacy-preserving negotiations. The proposed system operates exclusively on user hardware, enabling real-time bargaining while maintaining sensitive constraints locally. It integrates zero-knowledge proofs to ensure privacy and employs distilled world models to support advanced on-device reasoning. The architecture incorporates six technical components within an agentic AI workflow. Agents autonomously plan negotiation strategies, conduct secure multi-party bargaining, and generate cryptographic audit trails without exposing user data to external servers. The system is evaluated in insurance and B2B procurement scenarios across diverse device configurations. Results show an average success rate of 87%, a 2.4x latency improvement over cloud baselines, and strong privacy preservation through zero-knowledge proofs. User studies show 27% higher trust scores when decision trails are available. These findings establish a foundation for trustworthy autonomous agents in privacy-sensitive financial domains.
The tokenization of real-world assets (RWAs) has emerged as a transformative application of blockchain technology, with market projections estimating trillions of dollars in tokenized assets within the coming decade. However, a fundamental challenge remains unaddressed: physical assets such as precious metals, stored commodities, and warehoused goods incur structural negative carry -- custody, insurance, and audit costs that accumulate over time. While existing tokenization models have successfully established the market for digital gold and treasuries, they typically manage operational costs at the issuer level. The FRS introduces a framework to bring these economics directly on-chain, avoiding mechanisms such as token rebasing that compromise fungibility and composability with decentralized finance (DeFi) protocols. This paper proposes the Fungible Reserve Standard (FRS), a deterministic token design framework that encodes carrying costs transparently into on-chain logic. The FRS introduces an asset-per-token variable q(t) that decreases according to a predefined annualized carrying cost rate, coupled with a supply reconciliation mechanism that preserves holder balances and ERC-20 composability. While mathematically inspired by the daily expense ratio accrual in traditional asset management -- which often embed centralized profit margins -- the FRS design specifically encodes actual operational carrying costs to provide pure institutional-grade accounting clarity without compromising DeFi compatibility. The framework is asset-agnostic and applicable to any real-world asset with positive, predictable holding costs.
We formulate and solve stochastic control problems that model the core yield-generating strategy of the Ethena protocol, a decentralized finance (DeFi) stablecoin that earns yield by combining a long position in staked Ethereum (stETH) with an equal-sized short position in ETH perpetual futures. The combined position is delta-neutral with respect to the ETH spot price, yet earns carry from two sources: staking rewards on the stETH leg, and funding-rate payments received from long perpetual holders when the perpetual trades at a premium to spot. A key feature of our model is that the control -- the rate of simultaneously buying stETH and shorting the perpetual -- exerts two distinct types of price impact. \textit{Permanent} impact shifts the mid-market prices of both legs, compressing the basis and permanently eroding future funding income. \textit{Temporary} impact reflects execution slippage on each leg. We study both an infinite-horizon discounted problem and a finite-horizon problem in which the protocol maximizes total wealth up to a fixed date $T$, subject to a terminal cost for liquidating any remaining position. In both cases the optimal control is obtained explicitly.
Bitcoin's limited programmability and transaction throughput have historically prevented native Bitcoin from participating in decentralized finance (DeFi) applications. Existing solutions depend on honest-majority thresholds, or centralized custodial entities that introduce significant trust requirements. This paper introduces Bitcoin Smart Accounts (BSA), a novel protocol that enables native Bitcoin to access DeFi through trust-minimized infrastructure while maintaining self-custody of funds. BSA achieves this through a combination of emulated Bitcoin covenants using Partially Signed Bitcoin Transactions (PSBTs) and Taproot scripts, a Trusted Execution Environment (TEE)-based arbitration system, and destination chain smart contracts that enable DeFi platforms to accept self-custodial Bitcoin as collateral without necessitating protocol-level modifications. The setup leverages liquidity secured by the Lombard Security Consortium which provides a twofold advantage: for a DeFi protocol, liquidators rely on fungible assets with deep liquidity to quickly exit positions, while for a depositor, the general trust assumptions of honest majority (m-of-n) are reduced to existential honesty (1-of-k). We present the complete protocol design, including the Bitcoin architecture, the TEE-based arbitration mechanism, and the Smart Account Registry for protocol management. We provide a security analysis that demonstrates the correctness, safety, and availability properties under our trust model. Our design enables native Bitcoin to serve as collateral in lending markets and other DeFi protocols without requiring users to relinquish custody of funds.
Marco Dessalvi, Massimo Bartoletti, Alberto Lluch-Lafuente
Decentralized Finance (DeFi) has revolutionized financial markets by enabling complex asset-exchange protocols without trusted intermediaries. Automated Market Makers (AMMs) are a central component of DeFi, providing the core functionality of swapping assets of different types at algorithmically computed exchange rates. Several mainstream AMM implementations are based on the constant-product model, which ensures that swaps preserve the product of the token reserves in the AMM - up to a trading fee used to incentivize liquidity provision. Trading fees substantially complicate the economic properties of AMMs, and for this reason some AMM models abstract them away in order to simplify the analysis. However, trading fees have a non-trivial impact on users' trading strategies, making it crucial to develop refined AMM models that precisely account for their effects. In this work, we extend a foundational model of AMMs by introducing a new parameter, the trading fee ϕ ∈ (0,1], into the swap rate function. Fee amounts increase inversely proportional to ϕ. When ϕ = 1, no fee is applied and the original model is recovered. We analyze the resulting fee-adjusted model from an economic perspective. We show that several key properties of the swap rate function, including output-boundedness and monotonicity, are preserved. At the same time, other properties - most notably additivity - no longer hold. We precisely characterize this deviation by deriving a generalized form of additivity that captures the effect of swaps in the presence of trading fees. In particular, we prove that when ϕ < 1, executing a single large swap yields strictly greater profit than splitting the trade into smaller ones. Finally, we derive a closed-form solution to the arbitrage problem in the presence of trading fees and prove its uniqueness. All results are formalized and machine-checked in the Lean 4 proof assistant.
Do vulnerabilities in Decentralized Finance (DeFi) destabilize traditional short-term funding markets? While the prevailing ``Contagion Hypothesis'' posits that stablecoin reserve liquidations may transmit distress to traditional markets through fire-sale pressure, we document a short-horizon ``Flight-to-Quality'' pattern in the opposite direction. In the wake of major DeFi exploits, spreads on 3-month AA-rated commercial paper (CP) tend to narrow rather than widen. We interpret this pattern as consistent with a ``liquidity-recycling'' channel: capital leaving DeFi may be re-intermediated into traditional cash-management markets, with regulatory segmentation under SEC Rule 2a-7 making prime-eligible paper a plausible marginal destination. Because we do not directly observe daily fund-level routing into prime money market funds, this mechanism is inferred from pricing patterns and monthly holdings evidence rather than directly identified. The result is specific to exploit-driven operational shocks, this U.S. CP spread, and short event windows.
Non-fungible tokens (NFTs) on Ethereum currently follow a binary mobility paradigm: ERC-721 enables unrestricted transfers, whereas SBTs (ERC-5192) prohibit transfers entirely. We identify a design gap in which no standard mechanism supports bounded transferability, where ownership mobility is allowed but limited to a finite number of programmable transfers. We study counted NFT transfers and introduce ERC-7634 as a minimal realization compatible with ERC-721. The design augments each token with a transfer counter and configurable cap L, allowing ownership to evolve under a finite transfer budget. ERC-7634 defines a minimal extension interface with three lightweight functions (transferCountOf, setTransferLimit, and transferLimitOf), two events, and native-transfer hooks, requiring fewer than 60 additional lines of Solidity while preserving full backward compatibility with existing NFT infrastructure. We analyze behavioral and economic consequences of counted transfers. Our results reveal (i) a mobility premium induced by remaining transfer capacity, (ii) a protocol-level costing signal that can deter wash trading in cap-aware markets through irreversible budget consumption, (iii) bounded recursive collateralization enabled by limited ownership turnover, and (iv) associated security and gas-cost implications, including wrapper-bypass trade-offs. Evaluation on calibrated simulations shows that moderate limits (e.g., L = 10) affect fewer than 15% of tokens under representative transfer distributions, while repeated manipulation becomes unprofitable after a few cycles in a cap-aware pricing model; the additional gas overhead remains below 11% per transfer. We further position ERC-7634 within the NFT mobility design space, derive practical cap-selection guidelines, and discuss post-cap ownership outcomes including soulbound conversion, auto-burn, and provenance freeze.