Este artigo analisa o custo‑benefício energético de três mecanismos de consenso centrais no ecossistema de criptoativos: Proof‑of‑Work (PoW), Proof‑of‑Stake (PoS) e Proof‑of‑History (PoH) combinado a PoS, examinando como diferenças de consumo de energia se relacionam a segurança, desempenho e sustentabilidade econômica. A partir de dados recentes sobre consumo energético de redes públicas como Bitcoin, Ethereum antes e depois da transição para PoS e Solana, discute‑se em que medida a evolução dos mecanismos de consenso permite reduzir ordens de grandeza de uso de eletricidade por transação, sem necessariamente comprometer a segurança e a descentralização. A metodologia baseia‑se em revisão bibliográfica de estudos acadêmicos e relatórios técnicos sobre consumo de energia em blockchains, análise de estimativas consolidadas de uso anual de eletricidade e de energia por transação e discussão conceitual de trade‑offs entre eficiência energética, robustez criptográfica, requisitos de hardware e impactos regulatórios. Evidências indicam que o Bitcoin, ancorado em PoW, mantém consumo anual estimado em torno de 120–130 TWh, enquanto o Ethereum, após migrar para PoS em 2022, reduziu seu consumo em mais de 99%, passando a operar com menos de 0,01 TWh por ano. Relatórios de eficiência energética mostram que redes que combinam PoH e PoS, como a Solana, apresentam consumo de energia por transação da ordem de centenas de joules, inferior tanto a redes PoW quanto a muitas redes PoS, embora existam ressalvas metodológicas e discussões sobre centralização de infraestrutura. Conclui‑se que PoS e esquemas híbridos com PoH oferecem vantagens substanciais em termos de eficiência energética, mas a avaliação de custo‑benefício precisa incorporar conjuntamente segurança econômica, distribuição de poder, maturidade de ecossistema e alinhamento com agendas de sustentabilidade e descarbonização que tendem a moldar a evolução da infraestrutura Web3.<br>
We present a formal treatment of provenance trees, directed acyclic graphs of artifact registrations anchored immutably on a public blockchain, and introduce the operator trust problem: when a single privileged operator submits all on-chain registrations on behalf of users, the on-chain record alone cannot distinguish user-initiated registrations from unilateral operator actions. We resolve this through a dual-layer cryptographic commitment scheme in which two commitments derived from a single client-side secret key, binding the key to the tree root and to each unique registration identifier, make false attribution claims strictly dominated strategies. We prove correctness under standard cryptographic assumptions and establish honest behavior as the unique Nash equilibrium without relying on operator trust. We further introduce and analyze the tree poisoning problem: adversarial attacks on users' provenance trees via fraudulent root registration, malicious child attachment, and tree identity spoofing. We characterize the closure properties of each attack variant and prove that a complete provenance tree integrity model requires three distinct mechanisms: cryptographic priority, governance cascade, and contract enforcement, each necessary and none individually sufficient. The construction is deployed on Base (Ethereum L2) as AnchorRegistry, an immutable on-chain provenance registry. We provide gas complexity analysis demonstrating O(1) cost invariant to registry scale, and a trustless reconstruction algorithm recovering the complete registry from public event logs alone.
Daily probability changes in Kalshi macro prediction markets forecast cryptocurrency realized volatility through two distinct channels. The monetary policy channel, measured by Fed rate repricing on KXFED contracts, predicts Bitcoin volatility in sample with t = 3.63 and p < 0.001 but exhibits regime dependence tied to the 2024-2025 rate-cutting cycle. The recession risk signal from KXRECSSNBER proves more stable out of sample, delivering an MSFE ratio of 0.979 with Clark-West p = 0.020. The inflation channel, measured by CPI repricing on KXCPI contracts, predicts altcoin volatility for Ethereum, Solana, Cardano, and Chainlink with t-statistics ranging from -2.1 to -3.4 and out-of-sample gains for Ethereum at MSFE = 0.959 with p = 0.010 and Solana at p = 0.048. Both the Bitcoin--Fed-dovish and Chainlink--CPI specifications survive Benjamini-Hochberg correction at q = 0.05. Orthogonalization and baseline comparisons against Fed Funds futures, Treasury yields, and the Deribit implied volatility index confirm that these signals carry information not embedded in conventional financial instruments. The sample covers ten Kalshi event series and six cryptocurrency assets over January 2023 to March 2026.
Smart contract vulnerabilities can cause substantial financial losses due to the immutability of code after deployment. While existing tools detect vulnerabilities, they cannot effectively repair them. In this paper, we propose SCPatcher, a framework that combines retrieval-augmented generation with a knowledge graph for automated smart contract repair. We construct a knowledge graph from 5,000 verified Ethereum contracts, extracting function-level relationships to build a semantic network. This graph serves as an external knowledge base that enhances Large Language Model reasoning and enables precise vulnerability patching. We introduce a two-stage repair strategy, initial knowledge-guided repair followed by Chain-of-Thought reasoning for complex vulnerabilities. Evaluated on a diverse set of vulnerable contracts, SCPatcher achieves 81.5\% overall repair rate and 91.0\% compilation pass rate, substantially outperforming existing methods.
Over the past years, there has been increased risk of forging and replicating academic credentials unauthorized, and manipulation of data due to fast computerization of academic credentials. The traditional verification system that is centred on the Public Key Infrastructure (PKI), has included instances such as centralized control, the lack of transparency, and vulnerability to points of failures. Such challenges are suggesting a decentralized approach to the generation of digital certificates as well as their validation with the assistance of a blockchain Technology that is secure in nature. The suggested system will use cryptographic hashing, smart contracts using Ethereum and distributed ledger mechanisms to provide integrity, authenticity, and immutability of data. The blockchain has certificates in the hash values that can be easily verified and without the involvement of middle men. The framework will also enhance trust among the stakeholders as they will be in a position to ensure validation without disruption. As it is revealed through the experiment analysis and modular evaluation, the offered solution enhances the effectiveness of the verification towards its significant extent, the chance of fraud decrease, and offers a solution which can be further scaled and become suitable in the contemporary digital certification systems.
Yield-Aggregatoren automatisieren den Prozess des Yield-Farming im Bereich des Decentralized Finance (DeFi), indem sie Nutzerkapital bündeln und über verschiedene Protokolle hinweg einsetzen, um Renditen zu optimieren. Aufgrund ihrer hohen Komplexität sind ihre Funktionsweisen jedoch schwer nachzuvollziehen, und die Forschung zu ihren internen Mechanismen sowie den Interaktionen mit anderen Protokollen ist bislang begrenzt. Diese Arbeit adressiert diese Forschungslücke durch die Analyse zweier Ethereum-basierter Yield-Aggregatoren: Yearn Finance und Cian Yield Layer. Hierzu wurden Blockchain-Daten über einen Zeitraum von einem Jahr (4. Mai 2024 bis 3. Mai 2025) erhoben und ausgewertet, bestehend aus 2.459 Yearn-Transaktionen mit 5.575 Token-Transfers sowie 921 Cian-Transaktionen mit 1.963 Token-Transfers. Die Arbeit kombiniert eine operative Analyse, eine Netzwerkanalyse der Kapitalflüsse und einen Vergleich der Plattformmerkmale. Die Ergebnisse zeigen unterschiedliche Strategien: Yearn investiert Kapital überwiegend in Lending-Protokolle, indem es Liquidität zur Verfügung stellt, während Cian auf gehebeltes, rekursives Staking unter Einsatz von Flash-Loans setzt, um Restaking-Erträge zu erhöhen. Yearn hat eine breite Nutzerbasis mit vergleichsweise kleinen Einzeltransaktionen, während Cian eine kleinere Nutzerbasis besitzt, die von einem höheren Anteil großer Einzahlungen geprägt ist. Auf Grundlage der Analyse wurde ein konzeptionelles Modell entwickelt, das aus zwei miteinander verbundenen Lebenszyklen besteht: dem User-Lifecycle (Einzahlungen, Halteperiode, Auszahlungen) und dem Strategy-Management-Lifecycle (Kapitalallokation, Strategieausführung, Umschichtung). Dieses Modell erfasst die grundlegenden ökonomischen Funktionen von Yield-Aggregatoren unabhängig von ihrer technischen Implementierung. Die Arbeit liefert empirische Einblicke in die Funktionsweise von Yield-Aggregatoren, identifiziert DeFi-Protokolle als Investitionsziele und stellt ein konzeptionelles Modell zum Verständnis der Mechanismen von Yield-Aggregatoren vor.
This study investigates the rising security vulnerabilities in decentralized finance (DeFi) platforms from both technical and operational perspectives. Through literature review, case studies, and a comparative platform analysis, the research identifies the root causes, user impacts, and mitigation strategies for common security issues. Prominent incidents such as Ronin Network, Poly Network, Mango Markets, and Curve Finance are examined in depth, while security strategies of major DeFi platforms such as Aave, Compound, Uniswap, and Synthetix are compared. The study also discusses the implications of new technological developments like Ethereum Layer-2 solutions, Zero-Knowledge rollups, and account abstraction mechanisms on DeFi security. Findings emphasize that achieving a sustainable DeFi ecosystem requires a holistic approach involving not only technical safeguards but also transparent governance, user education and robust audit processes.
This paper examines the market maturation hypothesis in cryptocurrency markets through a three-stage analysis of the evolution of tail risk in Bitcoin (BTC) and Ethereum (ETH). Using daily closing prices from January 2015 to February 2026 for BTC (n = 4058) and November 2017 to February 2026 for ETH (n = 3015), we employ 365-day rolling windows—reflecting the continuous 24/7 operation of cryptocurrency markets—to trace the temporal dynamics of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and Maximum Drawdown (MDD). The empirical strategy combines (i) Newey–West trend tests on rolling risk metrics, (ii) regime-conditional analysis across market states (Bull, Bear, or Neutral) and volatility regimes (high/low uncertainty), and (iii) exceedance correlation analysis to capture asymmetric BTC–ETH tail dependence. The results are consistent with the market maturation hypothesis: all ten trend coefficients across both assets are statistically significant (p < 0.001), with linear time trends explaining up to 46.8% (BTC VaR1%) and 67.5% (ETH VaR1%) of variation in rolling tail risk. Sub-period comparisons confirm economically meaningful declines—BTC VaR1% fell by 22.0% and ETH VaR1% by 26.6% between the early and late subsamples. However, maturation is markedly asymmetric across uncertainty regimes: tail-risk reductions concentrate in low-uncertainty periods, whereas BTC MDD in high-uncertainty regimes shows no significant improvement (+1.0%, p = 0.176). Excess correlation analysis reveals a persistent and widening downside asymmetry (ρ− = 0.847 vs. ρ+ = 0.246 at the 90th percentile), with late-period upper-tail correlation turning negative (ρ+ = −0.175 at the 95th percentile), implying that portfolio diversification within the cryptocurrency asset class remains illusory during market stress. These findings carry direct implications for institutional risk management, stress-testing frameworks, and prudential regulation of digital assets.
Highlights • Identifying threats that cannot be countered by theoretical security based on STRIDE threat analysis of an existing provable data possession (PDP) system • Designing a PDP system with practical security features to counter threats that cannot be addressed with theoretical security alone • Presenting the implementation of the proposed PDP system, PRISM, which is also provided as an open-source software • Validating security properties through property-based fuzz testing with 10,000 randomized test runs per security property • Demonstrating PRISM’s key strengths through comprehensive experiments, including basic performance, trade-offs between processing time and data auditing efficiency, and capabilities for detecting data anomalies Digital platforms are increasingly recognized as a cornerstone for advanced virtual spaces such as smart cities and the metaverse, where vast amounts of data are aggregated, analyzed, and utilized to make critical decisions. These platforms rely on data fusion to integrate diverse sources of information, encompassing individual behavior, urban dynamics, and system states. Through auditing against data tampering, loss, and substitution, enabling the detection of such threats is critical to building a highly reliable system. This paper introduces PRISM (Provable and Immutable Storage Mechanism), an Ethereum-based Provable Data Possession (PDP) system designed to integrate data reliability and security with decentralized auditing. PDP, a cryptographic protocol that enables data integrity in untrusted cloud storage, has seen extensive research focusing on theoretical security and computational efficiency. PRISM extends this foundation by addressing practical security concerns, including the integration of authentication and authorization, data immutability, data uniqueness, data freshness, and state management, to ensure a robust system implementation. Experiments on processing costs and parameter analysis reveal a trade-off between the costs and detection accuracy and demonstrate that PRISM provides efficient data auditing.
Cloud storage systems have become an integral part of modern data management by enabling users to store and access data remotely.However, traditional cloud storage architectures rely on centralized servers, which introduce several critical challenges such as single-point failure, redundant data storage, increased storage costs, and security vulnerabilities.In earlier systems, data was stored in centralized data centers where duplicate files were repeatedly maintained, leading to inefficient utilization of storage resources.Although basic deduplication methods were implemented, they often compromised data confidentiality and lacked transparency in metadata management.Furthermore, failure of the central server could result in permanent data loss.To overcome these issues, this research proposes the Blockchain-enabled Heuristic Optimized Deduplication Model (BHODM), which integrates blockchain technology, InterPlanetary File System (IPFS), Convergent Encryption (CE), and heuristic-based chunking techniques.In this model, files are divided into optimized chunks based on file size using a heuristic approach.Each chunk is encrypted using CE, where the encryption key is derived from the hash of the data itself, allowing secure deduplication without exposing plaintext data.Duplicate chunks are identified through hash comparison, ensuring that only unique data is stored.The encrypted chunks are stored in IPFS, a decentralized peer-to-peer storage network, while metadata such as file names, block numbers, and hash values are securely maintained in an Ethereum blockchain smart contract, ensuring immutability and transparency.The system is implemented using Django, Web3, IPFS API, and AES-CTR encryption.Experimental results based on storage utilization and computation time demonstrate improved efficiency over traditional approaches.
Dr.B.Swathi Dr.B.Swathi, SAANIYA ARSHI, ARABOTHU ANVESH, MOHAMMED AYAAN AHMED · 5 authors
The quick adoption of blockchain technology and generative AI is a major factor in the world's electricity use, which raises concerns about their long-term environmental impact. To save energy, the first thing you need to do is figure out how much energy you are already using. But because blockchain and generative AI are both cloud-based services, it's not easy to understand how much energy they use when they're not at your site. This makes it harder for companies and organisations that want to improve the accuracy of calculating Scope 3 emissions. This study determines the energy consumption of these technologies at both the system level and per-use basis, comparing them to traditional services such as payment networks and web search engines. For instance, Bitcoin, which uses a Proof of Work (PoW) blockchain, uses about 121 TWh, or 0.43% of all the electricity used in the world. It also uses 720,000 times more energy per transaction than the Visa payment system. When Ethereum switched to Proof of Stake (PoS) in 2022, it used 99.988% less energy, showing how much more efficient things can be.Generative AI models also use a lot of energy, especially when they are being trained and used to make predictions. For instance, it took about 9,450 MWh of energy to train GPT-4, and it took more than 500 MWh of energy to do inference work every day. Inference, which is always powered by user activity, is often more resource-intensive than the training process. The authors say that we need to learn more about and lessen the environmental effects of these technologies right away. Possible solutions include energy-efficient consensus mechanisms or giving AIs the ability to better optimise their own lifecycle. The report is meant to help businesses think about how to use technology in a way that is good for the environment as part of a better or more complete Scope 3 emissions strategy.
Muhammad Ahmed, Adnan Ahmad, Furkh Zeshan, Sheeraz Akram
Blockchain-based systems increasingly require authentication mechanisms that simultaneously preserve user privacy, support accountability, and enable efficient credential revocation. However, most existing anonymous authentication schemes rely on pairing-based cryptography which introduce high computational overhead and limit deploy ability on widely adopted blockchain platforms such as Ethereum. This paper presents BAAR, a Blockchain-based Anonymous and Revocable authentication framework designed entirely within the discrete logarithm setting over the secp256k1 elliptic curve. BAAR integrates Pedersen vector commitments, Schnorr-based zero-knowledge proofs, and a Merkle-tree-based dynamic accumulator to support anonymous and unlinkable authentication with selective attribute disclosure and public, auditable revocation. Authentication and proof verification are performed off-chain, while the blockchain maintains only a compact revocation state, significantly reducing on-chain computation and gas costs. A formal security analysis demonstrates unforgeability, unlinkability, attribute privacy, and revocation soundness under standard cryptographic assumptions in the random oracle model. A prototype implementation on Ethereum confirms that BAAR achieves low gas consumption, logarithmic-time revocation, and scalable performance with respect to both the number of users and attributes. These results indicate that BAAR provides a practical balance between strong privacy guarantees and deploy ability, making it suitable for real-world blockchain-based identity and access-control systems.
Cryptocurrency prices often move with narratives and investor sentiment. This paper builds a multilingual crypto sentiment index, ML-CryptoSI, using daily news text in six languages and Binance market data for BTC and ETH. We first aggregate language-level daily sentiment and then use PCA to extract the common component across languages. Next, we test whether ML-CryptoSI predicts next-day returns and volatility proxies after controlling for lagged market conditions, liquidity, and day-of-week fixed effects. The results show that ML-CryptoSI has incremental information for returns, especially for ETH, and the effect is stronger on high news-intensity days. In contrast, the evidence for volatility prediction is weak in this short sample. Overall, the findings suggest that the common factor in multilingual news sentiment matters for short-run crypto pricing and is state dependent.
Blockchain users pay transaction fees to miners or block proposers who validate and add transactions to the distributed ledger. Ethereum introduces the concept of gas to decouple transaction costs from Ether's price volatility, calculating fees based on gas units. The current mechanism defined by Ethereum Improvement Proposal (EIP) 1559 dynamically adjusts the base fee according to block gas usage. However, its rule-based adjustment can lead to unstable gas consumption when demand fluctuates within a narrow range and struggles to respond efficiently to sudden demand spikes, such as during non-fungible token (NFT) drops. To address these limitations, we propose a deep reinforcement learning-based transaction fee mechanism that learns an adaptive base-fee update policy. Our approach maintains gas consumption close to the target level across various demand scenarios and stabilizes transaction fees and gas usage per block even under abrupt demand shifts. These results demonstrate that the proposed method provides a more adaptive and resilient fee adjustment mechanism compared to the current EIP-1559 model.
The environmental externalities of crypto-assets―particularly the substantial electricity demand inherent in the energy-inefficient consensus protocol known as Proof of Work（PoW）―have attracted increasing attention in sustainable finance. However, despite the growing interest in the environmental impact of crypto-assets, a comprehensive integration of technical energy analyses with environmental, social, and governance（ESG） investment, financial regulation, and corporate disclosure remains limited. This study addresses this gap by analysing how the environmental impact of crypto-assets can be incorporated into emerging sustainability frameworks, drawing on empirically verifiable sources such as the Cambridge Bitcoin Electricity Consumption Index, the Corporate Sustainability Reporting Directive of the European Union（EU）, the standards of the International Sustainability Standards Board, and disclosure information by major mining companies. The analysis is conducted along three dimensions:（1） market reactions and the evolution of ESG evaluation;（2） trends in disclosure regulation; and（3） case studies of corporate disclosure practices. Moreover, the transition to more energy-efficient alternative consensus protocols—as exemplified by Ethereum’s adoption of Proof of Stake—is examined as empirical evidence of technological innovation aimed at mitigating environmental externalities. By integrating market, institutional, and technological perspectives, the study elucidates persistent challenges concerning the reliability, comparability, and completeness of sustainability-related disclosures, as well as regulatory consistency across jurisdictions. It thereby contributes to the literature from both theoretical and practical perspectives.
This study presents an ontology-driven framework, EthOn, aimed at enhancing the operational efficiency of the Ethereum blockchain. Despite Ethereum’s prominence as a decentralized platform for executing smart contracts, it still faces significant limitations such as high gas fees, low throughput, and limited semantic processing capabilities. These challenges primarily stem from the platform's reliance on syntactic data structures and the absence of context-aware mechanisms in smart contracts. The proposed framework integrates semantic ontologies to structure blockchain data and enhance smart contract logic through context-driven reasoning. By embedding domain-specific ontologies into the Ethereum layer, EthOn enables intelligent data interpretation, optimized contract execution, and improved interoperability for decentralized applications (dApps). A prototype developed using Protégé, Node.js, Web3.js, Ganache Network and Apache Jena demonstrated notable advancements in both execution speed and gas efficiency. The results demonstrated remarkable improvements: Ethereum required an average block creation time of 10–15 seconds with gas consumption fluctuating between 9.6 million and 44.4 million units, while EthOn achieved block generation within 1–2 seconds and maintained gas usage between 423,630 and 3.7 million units. This represents an overall gas reduction of nearly 90% and a substantial improvement in execution speed.These results highlight the transformative potential of semantic technologies in reshaping blockchain infrastructure, paving the way for more scalable, efficient, and intelligent decentralized systems.
Under the influence of Industry 4.0, numerous scientific breakthroughs have emerged, including "cryptocurrencies and virtual currencies." Globally, various types of crypto-assets such as Ethereum, Litecoin, Bitcoin, Swisscoin, and Zcash have gained prominence, with regulatory approaches ranging from outright bans to formal authorization. This article examines the legal framework governing virtual currencies in Canada—a pioneer in establishing such regulations—to derive critical lessons for Vietnam in refining its legal framework for assets currently being drafted in the Law on Digital Technology Industry
The integrity of distributed healthcare databases is continuously threatened by unauthorized modifications, hardware faults, software vulnerabilities, and increasingly sophisticated cyberattacks. Traditional relational and NoSQL database systems rely on centralized access-control mechanisms and periodic audit logs that cannot provide cryptographic proof of unaltered history or real-time anomaly detection. This paper presents ChainGuard, a novel middleware framework that integrates Ethereum-based smart contracts, a network of IoT integrity-sensing nodes, and an AI-powered anomaly classifier to provide end-to-end, tamper-evident integrity assurance for distributed healthcare information systems. ChainGuard records SHA-256 hash digests of critical database state snapshots onto a permissioned Ethereum ledger, while a constellation of lightweight IoT agents embedded at database server nodes continuously monitors system-level indicators—I/O throughput, memory bus activity, and cryptographic nonce validation—at ten-second intervals. Deviations from baseline behavior trigger smart-contract-enforced quarantine procedures that freeze suspect transactions and emit verifiable incident records onto the immutable ledger. A Random Forest classifier trained on 12,000 synthetic database-event logs achieves 96.4% accuracy in discriminating legitimate bulk insertions from covert data-tampering attempts. Evaluation across three clinical deployment scenarios demonstrates that ChainGuard reduces mean time to tamper detection from 47 minutes (baseline centralized audit) to 38 seconds, with a false-positive rate of 1.7%. The framework is deployable on existing PostgreSQL and MongoDB infrastructure without schema modification, making it an immediately practicable path toward regulatory compliance with HIPAA, GDPR, and the forthcoming NIS2 Directive.
The growing adoption of the Electronic Health Records (EHR) has revolutionized healthcare information management. However, seamless and secure interoperability between different healthcare organizations continues to be a hard challenge. Data silos, centralized trust model, and lack of scalability are common impairments of traditional systems in care delivery which limit ‘patient centric’ way of care delivery. While blockchain technology offers decentralized trust and immutability, current solutions tend to be closed on a single blockchain platform, and thus not able to provide cross network interoperability and accessing data. To address this gap, this research introduces a Cross Chain EHR Sharing Framework that may be leveraged for the secure, bi-directional synchronization of EHR between Hyperledger Fabric (private blockchain) and Ethereum Sepolia Testnet (public blockchain) via decentralized storage by IPFS with AES 256 encryption. To facilitate interoperability the research introduces a smart middleware layer that autonomously monitors the blockchain events, processes encrypted Content Identifier (CID)s, enforces real time cross chain consistency and smart contract-based access control. The experimental evaluation shows that proposed framework achieves low synchronization times (< 195 ms), efficient blockchain operations with low gas and latency costs, small encryption overhead (< 4–5 KB), robust file storage and retrieval through IPFS. It also provides scalability, security and real-world applicability for the cross-chain healthcare interoperability.
The escalating energy consumption of blockchain networks has intensified concerns regarding their environmental sustainability, particularly in consensus protocols derived from Proof of Work. Although Proof of Stake improves efficiency, existing mechanisms remain static and lack responsiveness to dynamic network and energy conditions. This paper presents an Energy-Adaptive Consensus Mechanism (EACM) that integrates real-time workload awareness with energy-sensitive validator selection to optimize power utilization without compromising security. The proposed model introduces a multi-factor adaptive control layer that adjusts validation intensity based on transaction throughput, node availability, and energy profiles. A carbon awareness incentive function is incorporated to prioritize validators operating on renewable or low-carbon energy sources. Prototype implementation is developed on a private Ethereum-based test network, and comparative experiments are conducted against conventional Proof of Stake under variable workloads. Results indicate measurable reductions in energy consumption while maintaining competitive throughput, latency, and fault tolerance. The findings demonstrate that adaptive consensus design can enhance blockchain sustainability and provide a viable pathway toward carbon-efficient distributed ledger infrastructures.
Centralized stablecoins such as USDT and USDC enforce sanctions through contract-layer blacklist functions. Yet on public blockchains, a freeze is still an ordinary transaction competing with the sanctioned party's transfer for priority. It exposes a gap between contract-layer authority and ordering-layer enforcement: when both race for the same block, the outcome is set not by legal mandate, but by block producers' choices. Because both sides can pay for priority, sanction races create rents for block producers, which we call Sanction-Evasion MEV (SE-MEV). To measure this gap, we build the first longitudinal dataset of on-chain sanctions enforcement and evasion for Ethereum-based USDT and USDC from November 2017 to August 2025, covering more than $1.5 billion in frozen value. At least 7.3% of sanctioned USDT addresses and 18.7% of sanctioned USDC addresses had already been drained to zero before the freeze took effect. We also trace an escalation from issuer-side out-of-gas failures, to public gas auctions, private order flow, and direct payments to block producers, showing that block producers extract MEV from sanction enforcement. We then develop a game-theoretic model of stablecoin sanctions with MEV. It shows that compliant issuers cannot rationally stay outside the ordering market; fixed participation costs concentrate evasion among specialized MEV-aware adversaries; and the implicit MEV tax rises with regulatory penalties, creating incentives for vertical integration into block-building infrastructure. The problem extends beyond stablecoins. Any privileged on-chain action executed as an ordinary transaction -- emergency pauses, governance interventions, or judicial freezes -- faces the same conflict. Where ordering power follows economic incentives, ordering power is sanctioning power; contract-layer authority alone cannot guarantee enforcement.
Blockchain-based electronic voting systems that use zero-knowledge proofs (ZKPs) have been proposed as good candidates to provide both transparency and privacy of ballots. However, a fundamental challenge remains unmet in all existing schemes: the secure generation and protection of the voter's cryptographic secret key.In this paper, HME-KG (Hybrid Multi-Source Entropy Key Generation) is presented, a new credential derivation method which utilizes a cryptographically secure random salt, the national identity number of the voter and a per-device Client Device Secret (CDS) to derive a deterministic, brute-force-resistant secret key. HME-KG is integrated into BAVS-ZK, a complete anonymous blockchain voting framework employing AES-256-GCM encrypted credential storage, a Circom-based Groth16 zk-SNARK voting circuit, and on-chain nullifier verification via Ethereum Sepolia smart contracts. Security analysis demonstrates that HME-KG achieves voter determinism, cross-voter uniqueness, single-source failure resistance, and collision resistance under the security assumptions of SHA-256. Experimental evaluation on a 10,000-voter simulation confirms a 0.9998 scalability coefficient, 1.2-second proof generation, and 306,720 gas per vote—a 38.6% reduction compared to the Open Vote Network baseline. To the extent of current literature, BAVS-ZK is the first blockchain e-voting system to provide a complete, formally specified, and experimentally validated voter credential derivation and protection scheme.