András Nagy, János Tapolcai, István András Seres, Bence Ladóczki
Proof-of-stake consensus protocols often rely on distributed randomness beacons (DRBs) to generate randomness for leader selection. This work analyses the manipulability of Ethereum's DRB implementation, RANDAO, in its current consensus mechanism. Even with its efficiency, RANDAO remains vulnerable to manipulation through the deliberate omission of blocks from the canonical chain. Previous research has shown that economically rational players can withhold blocks known as a block withholding attack or selfish mixing when the manipulated RANDAO outcome yields greater financial rewards.
This paper introduces a novel gradient-based framework for maintaining protocol invariants in decentralized finance (DeFi) systems, representing a fundamental departure from traditional reactive monitoring approaches. Rather than detecting violations after they occur and adjusting system perceptions, this framework implements a proactive closed-loop control system that influences external market dynamics through calibrated protocol actions. The core innovation lies in tracking confidence gradients—including velocity, acceleration, and momentum—to predict potential invariant violations before they materialize. The system employs a nine-parameter calibration vector that dynamically adjusts based on confidence trends, enabling the protocol to respond appropriately to increasing (healthy), decreasing (crisis), or stable market conditions. During crisis scenarios, aggressive parameter adjustments trigger protocol actions such as enhanced lender incentives and borrower penalties, directly influencing market participant behavior to maintain critical constraints like maximum lending rates. The framework establishes mathematical foundations for confidence gradient metrics, parameter constraint spaces, and crisis level assessment. It demonstrates how calibrated algorithmic behavior generates measurable influence on external actors, effectively changing market reality rather than merely observing it. Theoretical analysis proves bounded confidence oscillations and probabilistic invariant maintenance guarantees under the proposed calibration schemes. Practical implementation considerations include smart contract architecture, gas optimization strategies through batched updates and fixed-point arithmetic, and parameter discretization for on-chain deployment. A numerical crisis response example illustrates the system's ability to prevent rate violations through coordinated supply increases and demand reductions. The paper positions this approach as foundational for the Kera Protocol ecosystem, with applications extending beyond DeFi lending to automated market makers, stablecoin protocols, governance systems, and cross-chain bridges. This paradigm shift from observation to calibrated influence represents a significant advancement in blockchain protocol stability mechanisms.
A. Marques, Luiz Eduardo Terra de Faria, Diogo S. Mendonça
The exponential growth of the cryptoasset market and the advancement of decentralized technologies have challenged traditional models of tax collection. In particular, self-custody wallets, which allow users to maintain direct control over their digital assets without the mediation of financial institutions, pose significant obstacles to oversight and tax compliance. This paper proposes an approach for the automatic collection of taxes on foreign exchange operations with stablecoins, using smart contracts on decentralized exchanges (DEXs). Through the implementation of a Proof of Concept (PoC), based on the Split Payment logic, we demonstrate the technical feasibility of applying a tax rate, analogous to the IOF, directly during transactions carried out by self-custody wallets, without the need for prior user identification. Experimental results, validated on the Polygon mainnet, reinforce the potential of the proposed model as a practical solution aligned with the principles of Web3, contributing to the debate on automated tax compliance mechanisms in decentralized environments.
This study investigates the dynamic relationship between order flow toxicity, measured by the volume-synchronized probability of informed trading (VPIN), and price jumps in the Bitcoin market using high-frequency data and vector autoregressive model (VAR) modelling. By integrating behavioral finance theory to market microstructure framework, we explore how informed trading activity influences jumps in price, and how traders respond to such volatility. Our findings reveal that VPIN significantly predicts future price jumps, with positive serial correlation observed in both VPIN and jump size, suggesting persistent asymmetric information and momentum effects. On the contrary, price jumps occasionally affect VPIN. This study also identifies time-zone and day-of-the-week effects in VPIN, highlighting the role of global trading patterns. The results are robust among the choices of jump tests including Jiang and Oomen (2008) test which is empirically robust against market microstructure noise. These results contribute to a deeper understanding of intraday volatility in cryptocurrency markets and offer practical implications for risk management, trading strategy design, and regulatory oversight.
Fuzzing is an effective technique to detect vulnerabilities in smart contracts. The challenge of smart contract fuzzing lies in the statefulness of contracts, which indicates that certain vulnerabilities can only be manifested in specific contract states. State-of-the-art fuzzers may generate and execute a plethora of meaningless or redundant transaction sequences during fuzzing, incurring a penalty in efficiency. To this end, we present DepFuzz , a hybrid fuzzer for efficient smart contract fuzzing, which introduces a symbolic execution module into the feedback-based fuzzer. Guided by the distance-based function dependencies between functions, DepFuzz can efficiently yield meaningful transaction sequences that contribute to vulnerability exposure or code coverage. The experiments on 286 benchmark smart contracts and 500 large real-world smart contracts corroborate that, compared to state-of-the-art approaches, DepFuzz achieves higher instruction coverage rate and uncovers many more vulnerabilities with less time.
Open access
Blockchain Technology Applications and Security
Auction Theory and Applications
Advanced Steganography and Watermarking Techniques
Decentralized coordination and digital contracting are becoming critical in complex industrial ecosystems, yet existing approaches often rely on ad hoc heuristics or purely technical blockchain implementations without a rigorous economic foundation. This study develops a mechanism design framework for smart contract-based resource allocation that explicitly embeds efficiency and fairness in decentralized coordination. We establish the existence and uniqueness of contract equilibria, extending classical results in mechanism design, and introduce a decentralized price adjustment algorithm with provable convergence guarantees that can be implemented in real time. To evaluate performance, we combine extensive synthetic benchmarks with a proof-of-concept real-world dataset (MovieLens). The synthetic tests probe robustness under fee volatility, participation shocks, and dynamic demand, while the MovieLens case study illustrates how the mechanism can balance efficiency and fairness in realistic allocation environments. Results demonstrate that the proposed mechanism achieves substantial improvements in both efficiency and equity while remaining resilient to abrupt perturbations, confirming its stability beyond steady state analysis. The findings highlight broad managerial and policy relevance for supply chains, logistics, energy markets, healthcare resource allocation, and public infrastructure, where transparent and auditable coordination is increasingly critical. By combining theoretical rigor with empirical validation, the study shows how digital contracts can serve not only as technical artifacts but also as institutional instruments for transparency, accountability, and resilience in high-stakes resource allocation.
Effective supply chain management under high-variance demand requires models that jointly address demand uncertainty and digital contracting adoption. Existing research often simplifies demand variability or treats adoption as an exogenous decision, limiting relevance in e-commerce and humanitarian logistics. This study develops an optimization framework combining dynamic Negative Binomial (NB) demand modeling with endogenous smart contract adoption. The NB process incorporates autoregressive dynamics in success probability to capture overdispersion and temporal correlation. Simulation experiments using four real-world datasets, including Delhivery Logistics and the SCMS Global Health Delivery system, apply maximum likelihood estimation and grid search to optimize adoption intensity and order quantity. Across all datasets, the NB specification outperforms Poisson and Gaussian benchmarks, with overdispersion indices exceeding 1.5. Forecasting comparisons show that while ARIMA and Exponential Smoothing achieve similar point accuracy, the NB model provides superior stability under high variance. Scenario analysis reveals that when dispersion exceeds a critical threshold (r > 6), increasing smart contract adoption above 70% significantly enhances profitability and service levels. This framework offers actionable guidance for balancing inventory costs, service levels, and implementation expenses, highlighting the importance of aligning digital adoption strategies with empirically observed demand volatility.
Decentralized Autonomous Organizations (DAOs) aim to enable participatory governance, but in practice face challenges of voter apathy, concentration of voting power, and misaligned delegation. Existing delegation mechanisms often reinforce visibility biases, where a small set of highly ranked delegates accumulate disproportionate influence regardless of their alignment with the broader community. In this paper, we conduct an empirical study of delegation in DAO governance off-chain discussions from 14 DAO forums. We develop a methodology to link forum participants to on-chain addresses, extract governance interests using large language models, and compare these interests against delegates' historical behavior. Our analysis reveals that delegations are frequently misaligned with token holders' expressed priorities and that current ranking-based interfaces exacerbate power concentration. We argue that incorporating interest alignment into delegation processes could mitigate these imbalances and improve the representativeness of DAO decision-making. To support future research, we will release our dataset and code in a public repository.
Introduction This study explores the feasibility of embedding futarchy, specifically policy-binding conditional prediction markets anchored to democratically chosen key performance indicators (KPIs) in Decentralized Science (DeSci) governance. By externalizing belief formation to speculative markets while anchoring values democratically, futarchy offers a structurally distinct alternative to existing Decentralized Autonomous Organization (DAO) governance models. Methods Through an empirical analysis of governance data from 13 DeSci DAOs, this study examines governance, participation, and cadence patterns that condition futarchic adoption. A retrospective simulation using proposals from VitaDAO assessed the degree to which historical decisions align with futarchy-preferred outcomes. Results The results indicate full directional alignment under deterministic modeling, suggesting latent compatibility between futarchy and existing DeSci governance. Discussion The analysis further outlines the design principles for implementation, emphasizing measurable KPIs and epistemic diversity. Futarchy, if carefully instantiated, may serve as a governance alternative for funding truth-tracking science through probabilistic decision making and market-based information aggregation.
Securities trading systems have settlement efficiency, audit transparency, and fraud prevention concerns due to centralized intermediaries and aging infrastructure. Existing research models risk counterparty trading due to delayed settlements, opaque record keeping, and human compliance checks. The study aims to design and evaluate a blockchain-based equities trading platform for transaction security and traceability. Provable Atomic Consensus for Trading (PACT), a blockchain-based architecture for regulated financial institutions' trading environments, combines hybrid consensus with a privacy-preserving cryptographic approach. A hybridized consensus process for efficient transaction finality, zero-knowledge proof enabled atomic settlements for instant delivery vs. payment while protecting commercial secrecy, and regulator-accessible smart contracts for real-time compliance checks are used in the PACT algorithm PACT found a 20% reduction in consensus finality time, 53% reduction in proof verification time, 56% improvement in smart contract vulnerability, and 42% improvement in auditability index on a permissioned blockchain with hardware-accelerated smart contracts. The study indicated 35.6% lower throughput and 41.7% lower Tx volume over 10 validators. Latency over 10 validators is 24% lower and Tx volume is 23.2% lower than existing research models. Blockchain improves securities infrastructure speed, reliability, and transparency without affecting compliance, according to studies.
Blockchain technology has driven the development of Decentralized Applications (DApps) in areas such as decentralized finance. However, as application scenarios become more complex, the limitations of computational resources and costs gradually lead to insufficient performance. Large Language Models (LLMs), as a promising technology, have the potential to enhance blockchain’s capabilities in complex task governance. However, due to factors such as consensus mechanisms, it is challenging to directly integrate them with blockchain. To address this issue, this article proposes and implements a general framework for integrating LLMs with blockchain data, C-LLM, which successfully overcomes interoperability barriers between the two. By combining semantic relevance evaluation and truth discovery techniques, this article presents an innovative data aggregation method, SenteTruth, which effectively improves the correctness and credibility of data generated by LLMs. To validate the framework’s effectiveness, we construct a dataset containing three types of questions, covering Q&A records between 10 oracle nodes and 5 LLM models. Experimental results show that, in the presence of 40% malicious nodes, the proposed method improves data correctness by an average of 17.74% compared with the optimal baseline. This research not only provides an innovative solution for the intelligent application of smart contracts but also demonstrates the potential for deep integration of LLMs and blockchain, driving the development of smarter and more complex application scenarios for smart contracts.
Amulyashree Sridhar, Kalyan Nagaraj, S. Ravi, Sindhu Kurup
The current research aims to discover applications of QML approaches in realizing liabilities within smart contracts. These contracts are essential commodities of the blockchain interface and are also decisive in developing decentralized products. But liabilities in smart contracts could result in unfamiliar system failures. Presently, static detection tools are utilized to discover accountabilities. However, they could result in instances of false narratives due to their dependency on predefined rules. In addition, these policies can often be superseded, failing to generalize on new contracts. The detection of liabilities with ML approaches, correspondingly, has certain limitations with contract size due to storage and performance issues. Nevertheless, employing QML approaches could be beneficial as they do not necessitate any preconceived rules. They often learn from data attributes during the training process and are employed as alternatives to ML approaches in terms of storage and performance. The present study employs four QML approaches, namely, QNN, QSVM, VQC, and QRF, for discovering susceptibilities. Experimentation revealed that the QNN model surpasses other approaches in detecting liabilities, with a performance accuracy of 82.43%. To further validate its feasibility and performance, the model was assessed on a several-partition test dataset, i.e., SolidiFI data, and the outcomes remained consistent. Additionally, the performance of the model was statistically validated using McNemar's test.
The unprecedented rise of Bitcoin has marked a significant milestone in the evolution of decentralized finance (DeFi). Despite Bitcoin's groundbreaking contributions, it faces inherent challenges due to its reliance on the Unspent Transaction Output (UTXO) model, which limits its capabilities in executing complex transactions and embedding diverse data types. To overcome these limitations, Ordinals and Inscriptions have been introduced, allowing extensive data and information embedding within Bitcoin transactions. Building upon these advancements, the recent development of the BRC-20 standard leverages these capabilities to introduce fungible tokens on the Bitcoin network. However, these innovations have yet to be fully integrated into the broader blockchain and DeFi ecosystems, and they still face challenges related to interoperability, scalability, and user perception. In this paper, we explore the intricacies of Ordinal, Inscription, and BRC protocols to address these challenges by analyzing their functionalities, operational methodologies, and potential applications. We offer a detailed examination of the challenges and future prospects, shedding light on the unexplored potential of these technologies in transforming Bitcoin transactions and expanding its role in the DeFi space. By thoroughly analyzing these new developments, we aim to bridge the gap in current academic research and offer valuable insights for developers, investors, and enthusiasts. This paper serves as a foundation for future innovations, paving the way for more robust, scalable, and user-friendly applications in the DeFi and Web3 landscape.
Eyal Briman, Nimrod Talmon, Angela Kreitenweis, Muhammad Idrees
Abstract The Optimism Retroactive Project Funding (RetroPGF) is a key initiative within the blockchain ecosystem that retroactively rewards projects deemed valuable to the Ethereum and Optimism communities. Managed by the Optimism Collective, a decentralized autonomous organization (DAO), RetroPGF represents a large-scale experiment in decentralized governance. Funding rewards are distributed in OP tokens, the native digital currency of the ecosystem. As of this writing, four funding rounds have been completed, collectively allocating over $100M, with an additional $1.3B reserved for future rounds. However, we identify significant shortcomings in the current allocation system, underscoring the need for improved governance mechanisms given the scale of funds involved. Leveraging computational social choice techniques and insights from multiagent systems, we propose improvements to the voting process by recommending the adoption of a utilitarian moving phantoms mechanism [1]. This mechanism was originally introduced by Freeman et al. [1], is designed to enhance social welfare (using the $$\ell _1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> norm) while satisfying strategyproofness–two key properties aligned with the application’s governance requirements. Our analysis provides a formal framework for designing improved funding mechanisms for DAOs, contributing to the broader discourse on decentralized governance and public goods allocation.
Recent research has introduced a decentralized autonomous organization (DAO) managing a house – No1s1 (no-ones-one). While the concept has been technically implemented, it remains open how to govern and maintain the house in a decentralized manner. In this study, we investigate how a DAO can govern and maintain physical assets. For this, we use a Design Science Research methodology coupled with a requirements-driven approach and demonstrate through an artifact how such a governance and maintenance system can be implemented. The proposed system features two tokens rewarding active interaction with the physical house leading to a local community. We evaluate the system with a scenario-based study with users showing how the community can govern and maintain the house. Thus, the paper contributes to a better understanding on how a DAO with physical assets can be governed and interact with these assets, and to the better understanding of blockchain governance in general.
Blockchain technology has reshaped digital finance, enabling decentralized applications (DApps) on platforms like Ethereum. However, these innovations have also facilitated fraudulent schemes such as Ponzi schemes, which deceive users with false promises of high returns. These schemes cause financial losses and weaken trust in blockchain systems. Existing detection methods face key challenges, including limited labeled data, over-reliance on transaction history, and failure to identify scams early. To address these issues, we propose a framework that combines static and dynamic features of smart contracts for early Ponzi detection. Our feature set includes opcode patterns, developer behavior, temporal trends, and metadata, crafted to work independently of transaction data. We enhance feature representation using TF-IDF, CountVectorizer, and Word2Vec for deeper semantic understanding. These features are used to train multiple machine learning and deep learning models such as Random Forest, XGBoost, CNNs, and BiGRUs. A stacking ensemble with a neural meta-learner integrates predictions for improved performance. The model achieves 99% accuracy and an AUC of 0.9522 on a curated Ethereum dataset, handling class imbalance through oversampling and synthetic data generation. We also employ SHAP for model explainability, offering insights into feature importance and promoting transparency. Our framework is scalable and supports real-time monitoring of contracts, helping prevent financial damage by detecting fraud at deployment. This solution enhances the security and reliability of decentralized finance platforms.
This paper presents the design and implementation of a decentralized electronic voting system based on a hybrid architecture that integrates the TRON blockchain with off-chain authentication mechanisms.The proposed solution employs smart contracts written in Solidity to record votes in an immutable and publicly auditable manner, while a backend service implemented in Node.js and a MySQL database handles voter authentication and enforces voter uniqueness.To prevent duplicate voting and ensure auditability, cryptographic hash functions are used to bind voter credentials and election parameters to each vote without exposing sensitive data on-chain.Experimental results demonstrate that the system effectively mitigates common security threats, such as duplicate voting and unauthorized data manipulation, while maintaining low transaction costs and practical usability.The findings indicate that the proposed hybrid approach provides a secure, transparent, and cost-effective alternative for electronic voting systems in real-world scenarios.
Current blockchain protocols (e.g., Proof-of-Work and Proof-of-Stake) secure the ledger yet cannot measure validator trustworthiness, allowing subtle misconduct that is especially damaging in decentralized-finance (DeFi) settings. We introduce Proof-of-Behavior (PoB), a consensus model that (i) gives each action a layered utility score -- covering motivation and outcome, (ii) adapts validator weights using recent scores, and (iii) applies decentralized verification with proportional slashing. The reward design is incentive-compatible, yielding a Nash equilibrium in which honest behavior maximizes long-run pay-offs. Simulated DeFi experiments (loan-fraud detection, reputation-weighted validation) show that PoB cuts fraud acceptance by more than 90%, demotes malicious validators within two rounds, and improves proposer fairness versus standard PoS, all with no more than a 5% throughput overhead. By linking consensus influence to verifiably trustworthy conduct, PoB offers a scalable, regulation-friendly foundation for secure and fair blockchain governance in financial applications.
Jonas Ballweg, Amir Kafshdar Goharshady, Zhaorun Lin
We consider the classical problem of running a decentralized and trustless auction, using a smart contract, on a programmable block-chain such as Ethereum. In our setting, there are n bidders who have paid a deposit to join the protocol. Each bidder i can make a bid 1 ≤ bi ≤ m and our goal is to find the highest bid (maxi bi) and its corresponding bidder (argmaxi bi) in a publicly-verifiable manner. Each bidder must be unaware of others' bids when making their own and should not be able to change their bid after having committed to it. Additionally, and most importantly, we aim to provide privacy to the losing bidders, ensuring that their bids remain undisclosed. This is particularly crucial in use-cases with repeated auctions in which knowledge of the bids in the previous auctions can affect the bidders' strategies. Formally, the information gained by any observer, whether a participant in the protocol or not, should precisely consist of the winning bid and its bidder and nothing more. We present a novel yet simple protocol for private sealed-bid auctions on the blockchain. Our protocol is decentralized and trustless. It is also both time- and gas-efficient. Our approach takes O(log m) time and costs O(log m) units of gas for each bidder. It also guarantees observational determinism with respect to all losing bids.
The security testing of Ethereum smart contracts has become increasingly important with the rise of decentralized applications (DApps) and blockchain technology. This systematic literature review (SLR) aims to provide a comprehensive overview of the state-of-the-art techniques, methodologies, tools, and challenges in the security testing of Ethereum smart contracts. By synthesizing and analyzing existing research articles, conference papers, and other relevant sources, this SLR identifies common trends, gaps, and areas for future research in this domain. The review covers various aspects of security testing, including vulnerability detection, testing frameworks, automated analysis tools, and best practices. In addition, it explores the impact of security vulnerabilities on smart contract ecosystems and proposes recommendations to improve the effectiveness and efficiency of security testing processes. This SLR serves as a valuable resource for researchers, practitioners, and developers interested in improving the security and reliability of Ethereum smart contracts.
Verifiable Delay Function (VDF) is a cryptographic concept that ensures a minimum delay before output through sequential processing, which is resistant to parallel computing. One of the significant VDF protocols academically reviewed is the VDF protocol proposed by Pietrzak. However, for the blockchain environment, the Pietrzak VDF has drawbacks including long proof size and recursive protocol computation. In this paper, we present an implementation study of Pietrzak VDF verification on Ethereum Virtual Machine (EVM). We found that the discussion in the Pietrzak's original paper can help a clear optimization in EVM where the costs of computation are predefined as the specific amounts of gas. In our results, the cost of VDF verification can be reduced from 4M to 2M gas, and the proof length can be generated under 8 KB with the 2048-bit RSA key length, which is much smaller than the previous expectation.
The evolution of blockchain technology, from its origins as a decentralized ledger for cryptocurrencies to its broader applications in areas like decentralized finance (DeFi), has significantly transformed financial ecosystems while introducing new challenges such as Maximum Extractable Value (MEV). This paper explores MEV on the Polygon blockchain, with a particular focus on Atomic Arbitrage (AA) transactions. We establish criteria for identifying AA transactions and analyze key factors such as searcher behavior, bidding dynamics, and token usage. Utilizing a dataset spanning 22 months and covering 23 million blocks, we examine MEV dynamics with a focus on Spam-based and Auction-based backrunning strategies. Our findings reveal that while Spam-based transactions are more prevalent, Auction-based transactions demonstrate greater profitability. Through detailed examples and analysis, we investigate the interactions between network architecture, transaction sequencing, and MEV extraction, offering comprehensive insights into the evolution and challenges of MEV in decentralized ecosystems. These results emphasize the need for robust transaction ordering mechanisms and highlight the implications of emerging MEV strategies for blockchain networks.
Blockchains support a rapidly growing digital economy. Through decentralization, they enable the ownership and transfer of digital assets without centralized intermediaries while resisting attacks, faults, and collusion. Yet decentralization is not guaranteed and must be sustained against external shocks. What design choices help blockchains maintain decentralization when disrupted? We find that resource flexibility, how easily consensus resources can be moved and redeployed, is key to sustained decentralization. Using three real-world shocks—China’s 2021 crypto mining ban, Hetzner’s 2022 shutdown of Solana validators, and Ethereum’s transition from proof-of-work to proof-of-stake—we show that blockchains whose consensus resources are more flexible recover decentralization faster. These findings offer actionable guidance. Blockchain designers should consider resource flexibility as a first-order design parameter. Operators should diversify infrastructure across jurisdictions to reduce vulnerability to localized shocks. Policymakers should be aware that regulations targeting blockchains can have unintended effects on decentralization.
In online auctions, fraudulent behaviors such as shill bidding pose significant risks. This paper presents a conceptual framework that applies dynamic, behavior-based penalties to deter auction fraud using blockchain smart contracts. Unlike traditional post-auction detection methods, this approach prevents manipulation in real-time by introducing an economic disincentive system where penalty severity scales with suspicious bidding patterns. The framework employs the proposed Bid Shill Score (BSS) to evaluate nine distinct bidding behaviors, dynamically adjusting the penalty fees to make fraudulent activity financially unaffordable while providing fair competition. The system is implemented within a decentralized English auction on the Ethereum blockchain, demonstrating how smart contracts enforce transparent auction rules without trusted intermediaries. Simulations confirm the effectiveness of the proposed model: the dynamic penalty mechanism reduces the profitability of shill bidding while keeping penalties low for honest bidders. Performance evaluation shows that the system introduces only moderate gas and latency overhead, keeping transaction costs and response times within practical bounds for real-world use. The approach provides a practical method for behaviour-based fraud prevention in decentralised systems where trust cannot be assumed.