Blockchain Papers

Mikio Hanaeda

No abstract is available for this record.

Ayush Dharaiya, Rimmi Sharma, Mahek Jain, Lakshit Pathak · 6 authors

No abstract is available for this record.

Dr.Scient. M.Phil. M.D. Paul A. Torvund

No abstract is available for this record.

Francesco Saverio Canepa

No abstract is available for this record.

Hyungjun Cho

No abstract is available for this record.

S. P. Siddique Ibrahim, Thallapaka Nikhila, Rohitha Kotagiri, Kondreddy Manvitha

No abstract is available for this record.

Changxiao Huang

No abstract is available for this record.

Dongxu Li, Yang Ji, yr N

No abstract is available for this record.

P. Meena Kumari, P. Jagadish Kumar, K. Bharathi

No abstract is available for this record.

Yackolley Amoussou-Guenou, Emmanuelle Anceaume, Antonella Del Pozzo, Aimen Djari · 6 authors

This paper introduces a secure state-sharding solution for permissionless blockchains that supports both payment transactions and general Ethereum-like smart contracts. The secure partitioning of the blockchain into distinct shards continually adapts to the current transaction workload, encompassing both user and smart contract transactions. A novel two-phase commit protocol guarantees the correct execution of smart contracts distributed across different shards. Notably, this protocol offers, for the first time, support for smart contracts with nested calls across shards, without any limitation on the depth of the calling graph. An experimental study highlights the benefits of the adaptive behavior of shards during the distributed execution of such nested smart contracts.

Chonghe Zhao, Deen Ma, Xicheng Zhang, Taotao Wang · 5 authors

Ethereum is a leading blockchain system whose security and performance depend critically on efficient transaction propagation. Currently, to curb bandwidth, Ethereum employs a hybrid protocol wherein nodes eager-push full transactions to a random peer subset and announce hashes to the remainder for on-demand lazy-pull. Yet, our MainNet measurements reveal that this strategy suffers from “blind” forwarding—indiscriminately transmitting full transactions without evaluating receiver acceptance. This causes peers to receive multiple redundant copies of non-viable transactions that are ultimately discarded by local pools. Also, randomized peer selection results in substantial redundant transmission, excessive hops, and a high lazy-pull rate (the proportion of transactions acquired via the slower request-response mechanism). These two inefficiencies collectively prolong propagation latency and limit further bandwidth savings. To address these limitations, we propose BELLTP, aBandwidth-Efficient andLow-LatencyTransactionPropagation protocol that incorporates two key awareness modules.Transaction Awarenessclassifies transactions into “urgent” and “leisure” using a lightweight predictor trained on Mainnet data to forecast receiver acceptance. Based on this, leisure transactions (with low predicted acceptance) are propagated via full lazy-pull to ensure that each node receives the full payload once, while urgent transactions utilize strategically targeted eager-push guided by node awareness to rectify random selection inefficiencies.Node Awarenessidentifies hub and common nodes for urgent transaction propagation by combining a theoretical network model, enabling hub nodes to eagerly push to all peers (shortening hop count while maintaining low redundant transmission) and common nodes to leverage a peer need score to eagerly push only to high-need peers (lowering lazy-pull rate). Extensive experiments on Ethereum MainNet and in a controlled local testbed demonstrate that BELLTP significantly outperforms the current protocol in both bandwidth consumption and propagation latency.

Giacomo Vella, Daniel Trabucchi, Valeria Portale, Luca Gastaldi

Digital platforms follow a predictable trajectory: as they mature and accumulate network effects, they progressively centralize control, extract value from ecosystem participants, and constrain complementor autonomy, a pattern this study terms the "platform trap." Yet Ethereum, a blockchain-based platform now in its second decade of operation, has deliberately resisted this trajectory while fostering thousands of decentralized applications and a global developer community. This study investigates how a platform ecosystem can sustain innovation and architectural evolution in the absence of a central orchestrator. Drawing on a longitudinal qualitative case study of Ethereum (2013 to 2025), based on 67 archival sources and 227 first-order codes organized through the Gioia methodology, three governance mechanisms are identified that collectively constitute non-degenerative platform governance: (1) deliberate value capture restraint, through which the platform's steward organization forgoes extractive revenue models; (2) protocol-level rule codification, whereby governance rules are embedded in verifiable code and consensus mechanisms rather than managerial discretion; and (3) continuous stakeholder realignment, achieved through institutionalized multi-venue deliberation and adaptive crisis response. The study challenges the assumption that centralized orchestration is a prerequisite for ecosystem coherence and introduces decentralized platform stewardship as an alternative governance archetype.

Ye Tian, Liangliang Song, Yuanyuan Ma, Yanbin Wang · 7 authors

No abstract is available for this record.

Marwa Mnasri, Afef Jmal Maâlej, Mohamed Jmaïel

No abstract is available for this record.

Zhen Chen, Fang Zhou, Bo Liu, Haixia Long · 5 authors

In recent years, phishing scams have become one of the most rampant criminal activities on Ethereum, causing significant financial losses to investors and disruptions to the Ethereum ecosystem. Existing phishing scam detection methods typically model Ethereum transaction records as graphs, extracting features from paired nodes based on the topological relationships. However, these methods mostly focus on low-order relational aspects, neglecting higher-order structural information in the network. In this paper, we propose a new method — Ethereum Phishing Scam Detection by Higher-Order Topology (EPSD-HOT), which improves phishing scam detection performance by mining higher-order topological features from the network. We conduct experiments on a public dataset and a crawled real-world dataset, extracting ten subgraphs with distinct network characteristics. The experimental results show that the average AUC-ROC for the ten subgraphs is 0.9970, with improvements ranging from 0.0181 to 0.1658 compared to baseline methods. This indicates that our approach is highly robust and can effectively detect phishing scams across different subgraphs while overcoming the issue of class imbalance. By incorporating higher-order structural information into node features, this work offers new insights for enhancing phishing scam detection in Ethereum.

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.

Bartosz Bieganowski, Robert Ślepaczuk

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.

Shuyi Miao, Wangjie Qiu, Xiaofan Tu, Yunze Li · 6 authors

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.

Supriya Khadka, Dhiman Goswami, Sanchari Das

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.

Junhyuk Lee

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.

Tomasz Mroziewicz, Robert Ślepaczuk

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.