Alison Gonçalves Schemitt, Henrique Fan da Silva, Roben Castagna Lunardi, Diego Kreutz · 6 authors
The advent of quantum computing poses a threat to the security of traditional encryption algorithms. This has motivated the development of post-quantum cryptography (PQC). In 2024, the National Institute of Standards and Technology (NIST) standardized several PQC algorithms, marking an important milestone in the transition toward quantum-resistant security. Blockchain systems fundamentally rely on cryptographic primitives to guarantee data integrity and transaction authenticity. However, widely used algorithms such as ECDSA, employed in Bitcoin, Ethereum, and other networks, are vulnerable to quantum attacks. Although adopting PQC is essential for long-term security, its computational overhead in blockchain environments remains largely unexplored. In this work, we propose a methodology for benchmarking both PQC and traditional cryptographic algorithms in blockchain contexts. We measure signature generation and verification times across diverse computational environments and simulate their impact at scale. Our evaluation focuses on PQC digital signature schemes (ML-DSA, Dilithium, Falcon, Mayo, SLH-DSA, SPHINCS+, and Cross) across security levels 1 to 5, comparing them to ECDSA, the current standard in Bitcoin and Ethereum. Our results indicate that PQC algorithms introduce only minor performance overhead at security level 1, while in some scenarios they significantly outperform ECDSA at higher security levels. For instance, ML-DSA achieves a verification time of 0.14 ms on an ARM-based laptop at level 5, compared to 0.88 ms for ECDSA. We also provide an open-source implementation to ensure reproducibility and to encourage further research.
Samuel Oleksak, Richard Gazdik, Martin Peresini, Ivan Homoliak
Proof of Work (PoW) is widely regarded as the most secure permissionless blockchain consensus protocol. However, its reliance on computationally intensive yet externally useless puzzles results in excessive electric energy wasting. To alleviate this, Proof of Useful Work (PoUW) has been explored as an alternative to secure blockchain platforms while also producing real-world value. Despite this promise, existing PoUW proposals often fail to embed the integrity of the chain and the identity of the miner into the puzzle solutions, not meeting the necessary requirements for PoW and thus rendering them vulnerable. In this work, we propose a PoUW consensus protocol that computes client-outsourced SNARK proofs as a byproduct, which are simultaneously used to secure the consensus protocol. We further leverage this mechanism to design a decentralized marketplace for outsourcing SNARK proof generation, which is, to the best of our knowledge, the first such marketplace operating at the consensus layer while meeting all necessary properties of PoW.
With the proliferation of decentralized applications (DApps), the conflict between the transparency of blockchain technology and user data privacy has become increasingly prominent. While Decentralized Identity (DID) and Verifiable Credentials (VCs) provide a standardized framework for user data sovereignty, achieving trusted identity verification and data sharing without compromising privacy remains a significant challenge. This paper proposes a novel, comprehensive framework that integrates DIDs and VCs with efficient Zero-Knowledge Proof (ZKP) schemes to address this core issue. The key contributions of this framework are threefold: first, it constructs a set of strong privacy-preserving protocols based on zk-STARKs, allowing users to prove that their credentials satisfy specific conditions (e.g., "age is over 18") without revealing any underlying sensitive data. Second, it designs a scalable, privacy-preserving credential revocation mechanism based on cryptographic accumulators, effectively solving credential management challenges in large-scale scenarios. Finally, it integrates a practical social key recovery scheme, significantly enhancing system usability and security. Through a prototype implementation and performance evaluation, this paper quantitatively analyzes the framework's performance in terms of proof generation time, verification overhead, and on-chain costs. Compared to existing state-of-the-art systems based on zk-SNARKs, our framework, at the cost of a larger proof size, significantly improves prover efficiency for complex computations and provides stronger security guarantees, including no trusted setup and post-quantum security. Finally, a case study in the decentralized finance (DeFi) credit scoring scenario demonstrates the framework's immense potential for unlocking capital efficiency and fostering a trusted data economy.
Electronic waste (e-waste) is a rapidly growing global problem caused by shorter device lifecycles and rising consumption. India ranks third globally in e-waste generation, producing over 1.7 million tonnes in 2023-24, of which less than half is formally processed. To address this, we propose Green Grid, an integrated AI-powered e-waste management platform combining IoT-enabled smart collection, AI-based device classification, blockchain-based traceability, and gamified citizen engagement. The system features smart recycling bins with sensors for real-time monitoring, deep learning models for device identification and sorting, a blockchain ledger for tamper-proof tracking, and a reward-based mobile or web app to encourage user participation. Additionally, Green Grid offers analytics dashboards and an eco-marketplace to support policymakers and recyclers. By bridging technology, sustainability, and community participation, the platform enhances transparency, increases formal recycling rates, and advances India's transition toward a circular economy.
As blockchain systems grow in complexity, secure and efficient smart contract development remains a crucial challenge. Large Language Models (LLMs) like DeepSeek promise significant enhancements in developer productivity through automated code generation, debugging, and testing. This study focuses on Solidity, the dominant language for Ethereum smart contracts, where correctness, gas efficiency, and security are critical to real-world adoption. This study evaluates the capabilities of DeepSeek’s V3 and R1 models, a non-reasoning Mixture-of-Experts architecture and a reasoning-based model trained via reinforcement learning, respectively, in automating Solidity contract generation and testing, as well as identifying and fixing common vulnerabilities. We designed a controlled experimental framework to evaluate both models by generating and analysing a diverse set of smart contracts, including standardised tokens (ERC20, ERC721, ERC1155) and real-world application scenarios (Supply Chain, Token Exchange, Auction). The evaluation is grounded on a multidimensional metric suite covering quality, technical robustness and process characteristics. Vulnerability detection and patching capabilities are tested using predefined vulnerable contracts and guided patch prompts. The analysis spans six levels of prompt complexity and compares the impact of reasoning-based and non-reasoning-based generation strategies. Findings reveal that R1 delivers more accurate and optimised outputs under high complexity, while V3 performs more consistently in simpler tasks with simpler code structures. However, both models exhibit persistent hallucinations, limitations in vulnerability coverage, and inconsistencies due to prompt formulation. The correlation between re-evaluation patterns and output quality suggests that reasoning helps in complex scenarios, although excessive revisions may lead to over-engineered or unstable solutions. Neither model is robust enough to autonomously generate issue-free smart contracts in complex or security-critical scenarios, underscoring the need for human oversight. These findings highlight best practices for integrating LLMs into blockchain development workflows and emphasise the importance of aligning model selection with task complexity and security requirements.
The Buru Regency Government, as the party tasked with administering government, development, and public services, is required to report on regional financial accountability as the basis for assessing its financial performance. The purpose of this study is to assess regional financial performance using ratios from 2020 to 2024, consisting of: Regional Fiscal Independence; Effectiveness of PAD Management; Effectiveness of Regional Taxes; Degree of Fiscal Decentralization; Fiscal Dependency; and Growth of Regional Government Finance in Buru Regency. Using secondary data sourced from the Ministry of Finance website, this study concludes that the financial performance of the Buru Regency Government consists of: 1) the regional fiscal autonomy ratio is still very low with an instructive relationship pattern, indicating that the local government is not yet capable of financing its own government activities, development, and services to the community, and the local government still needs intervention from the central government; 2) the fiscal decentralization ratio indicates that the local government's ability to increase its own revenue (PAD) to finance its own development is still very limited; 3) the local tax effectiveness ratio and local revenue (PAD) indicate that the local government is less effective in realizing tax revenue and local revenue (PAD) from the set targets and real potential; 4) The fiscal dependency ratio shows that the Buru Regency local government is still highly dependent on assistance from the central and provincial governments compared to its own regional revenue; 5) The PAD growth ratio shows that the local government is poor/negative in maintaining and increasing PAD.
J. Balamurugan, Devineni Poojitha, R Bindu, Archana Pallakonda · 8 authors
Decentralized energy trading has been designed as a scalable substitute for traditional electricity markets. While blockchain technology facilitates efficient transparency and automation for peer-to-peer energy trading, the majority of current proposals lack real-time intelligence and adaptability concerning pricing strategies. This paper presents an innovative machine learning-driven solar energy trading platform on the Ethereum blockchain that uniquely integrates Bayesian-optimized XGBoost models with dynamic pricing mechanisms inherently incorporated within smart contracts. The principal innovation resides in the real-time amalgamation of meteorological data via Chainlink oracles with machine learning-enhanced price optimization, thereby establishing an adaptive system that autonomously responds to fluctuations in supply and demand. In contrast to existing static pricing methodologies, our framework introduces a multi-faceted dynamic pricing model that encompasses peak-hour adjustments, prediction confidence weighting, and weather-influenced corrections. The system dynamically establishes energy prices predicated on real-time supply–demand forecasts through the implementation of role-based access control, cryptographic hash functions, and ongoing integration of meteorological and machine learning data. Utilizing real-world meteorological data from La Trobe University’s UNISOLAR dataset, the Bayesian-optimized XGBoost model attains a remarkable prediction accuracy of 97.45% while facilitating low-latency price updates at 30 min intervals. The proposed system delivers robust transaction validation, secure offer creation, and scalable dynamic pricing through the seamless amalgamation of off-chain machine learning inference with on-chain smart contract execution, thereby providing a validated platform for trustless, real-time, and intelligent decentralized energy markets that effectively address the disparity between theoretical blockchain energy trading and practical implementation needs.
Oct 10, 2025·Companion Proceedings of the 2025 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity
This proposal presents a multi-layer dynamic security framework for protecting DeFi smart contracts against evolving attack vectors that traditional static analysis and security audits fail to detect. We observed that many DeFi exploits succeed not due to source code bugs, but because of flawed assumptions about user behaviors and external dependencies that only manifest at runtime. Our system provides three complementary additional defenses to significantly increase the difficulty of launching successful attacks: (1) CrossGuard, a control-flow integrity mechanism that only whitelists legitimate invocation patterns;(2) Trace2Inv, a runtime invariant generator that learns and enforces invariants from historical transaction data; and (3) an ecosystem-wide risk analysis tool that detects compositional vulnerabilities in protocol dependencies. By leveraging upgradeable contracts, the framework can progressively refine defenses as protocols stabilize. Our evaluation results show blocking 85% of past exploits with under 1% false positives and below 20% gas overhead.
This paper presents EMDns, a domain name system based on Ethereum and MongoDB, addressing the centralization, security weaknesses, and privacy issues of the Domain Name System (DNS). EMDns enables automated and decentralized domain management to eliminate SPOF and authority dependence, while resolution is performed via local look-ups with hash comparison to protect privacy against deanonymization attacks. Experimental results show that EMDns maintains resolution efficiency while significantly enhancing security and privacy.
General Background: Blockchain-based smart contracts have revolutionized global transactions by enabling automatic, transparent, and decentralized execution of agreements. Specific Background: Despite their efficiency, these digital instruments challenge traditional private international law, particularly regarding jurisdiction, applicable law, and enforceability in cross-border contexts. Knowledge Gap: Existing legal systems, especially in the Middle East, lack comprehensive frameworks to address decentralized contracting and blockchain-based evidence. Aims: This study critically examines the intersection between smart contracts and conflict of laws in digital environments, focusing on Iraq’s legal framework and regional comparison with the EU and the US. Results: The analysis reveals that while the EU has developed coherent regulatory models such as MiCA and the Data Act, and several US states have recognized smart contracts’ validity, Iraq’s Civil Code of 1951 remains inadequate to regulate automated digital agreements. Novelty: The paper proposes a unified legal model integrating UNCITRAL’s 2024 Model Law on Automated Contracting, regional cooperation through the Arab League and GCC, and legislative reforms in Iraq to recognize blockchain evidence. Implications: Implementing such a framework would harmonize technological progress with legal certainty, enhance cross-border trust, and position Iraq and the Middle East within the global digital economy.Highlight : Analyzes the intersection of smart contracts and conflict of laws in digital space. Examines Iraq’s outdated legal framework amid rapid technological change. Suggests adopting international models and regional cooperation for legal reform. Keywords : Smart Contracts, Blockchain, Conflict of Laws, Private International Law, Jurisdiction, Iraq.
Abdul Malik, Gayatri Putri, Hesti Putri, Ahmad Badruddin
The proliferation of crypto-assets has raised critical questions about their impact on global financial stability. This study rigorously investigates the structural evolution of the cryptocurrency market's role within the global financial system, testing the hypothesis that it has transitioned from a peripheral, shock-absorbing entity into a systemically significant transmitter of financial risk. We employ a Time-Varying Parameter Vector Autoregression (TVP-VAR) model on daily data from January 1, 2017, to December 31, 2024, examining the dynamic connectedness between a bespoke, rebalanced cryptocurrency index (CRIX20) and key global financial indicators (S&P 500, MSCI World, VIX, DXY). The econometric framework utilizes a Bayesian estimation approach with standard priors, a 200-day rolling window, and a 10-day forecast horizon for Generalized Forecast Error Variance Decompositions (GFEVD). Methodological robustness is confirmed through structural break tests and sensitivity analysis of the forecast horizon. Our findings reveal a profound structural transformation. Prior to mid-2020, the cryptocurrency market was a consistent net receiver of financial spillovers. A structural break, formally identified in the third quarter of 2020, marks a definitive regime shift. Post-break, the crypto market has become a significant and persistent net transmitter of risk to the traditional financial system. The total connectedness index for the entire system shows a marked secular increase, with the crypto market's contribution to systemic risk growing substantially. Gross spillover analysis confirms this shift is driven by a dramatic increase in risk transmission from the crypto market to other assets. In conclusion, the cryptocurrency market can no longer be considered an isolated ecosystem; it is now an integral and potentially destabilizing component of the global financial architecture. The era of crypto-assets as reliable diversifiers has waned, replaced by a new reality where shocks originating within this market pose a credible threat to broader financial stability. These findings present urgent challenges for regulatory oversight, systemic risk monitoring, and portfolio management.
Purpose: The purpose of this research is to explore how new technologies, such as DLT (distributed ledger technology), ML (machine learning) and AI (artificial intelligence), can support green economic growth and sustainable finance. Need for the study: Awareness of environmental challenges highlights the importance of using technology to support and promote sustainable financial practices. Therefore, this study, among other things, aims to explore this importance by analysing how AI, ML and DLT can contribute to continuously improving innovation and efficiency in various sustainable finance projects. Methodology: This study uses a literature review technique to examine technology use, sustainable finance, and the transition to a sustainable economy. To achieve this goal, qualitative interviews were conducted with professionals in the fields of sustainability, technology and finance to explore different practices and identify different strategies for developing the future of the finance industry. Findings: Based on the findings of previous studies, AI, ML and DLT play an important role in improving risk management, increasing transparency and simplifying procedures that serve to make decisions in the sustainable finance sector. The transformation to a green and sustainable economy can be more straightforward if it relies on the ability of these important technologies to incorporate some ESG (environmental, social and governance) considerations into organisations’ plans for potential investments. Practical implications: The study's findings will help software developers, financial institutions and policymakers promote and strengthen sustainable development. Furthermore, the use of technology, especially advances in AI, ML and DLT, offers various valuable perspectives for those engaged in the transition to more environmentally friendly financial practices, contributing to creating a more sustainable global economy.
Ifran Khan, Huangbao Gui, Chin Man Chui, Mrs Faryal · 6 authors
This study investigates the dynamic volatility transmission between leading cryptocurrencies (Bitcoin, Ethereum, and Binance Coin) and major Chinese firms in the technology (Tencent and Alibaba), green energy (CATL, BYD, and LONGi), and traditional energy (PetroChina) sectors, including the CSI 300 index. Employing the frameworks of Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) on daily data from July 2018 to May 2025, we demonstrate significant cross-market risk transmission. The total connectedness index averages 34.77%, soaring to over 50% during the COVID-19 crisis, underscoring heightened systemic vulnerability. Our key finding identifies the CSI 300 index and cryptocurrencies (BTC, ETH) as the primary net transmitters of volatility shocks, whereas Chinese tech and energy firms (Tencent, CATL, and PetroChina) act as the main net receivers. A critical insight from the frequency decomposition is the absolute dominance of short-term spillovers (1–4 days), which constitute 34.85% of total connectedness, vastly outweighing the minimal effects in the medium- (4–10 days: 0.78%) and long-term (beyond 10 days: 0.52%). Investor sentiment, speculation, and news shocks drive short-term volatility spillovers from cryptocurrencies to stocks, particularly evident in their strong correlation with Chinese tech and energy equities. We attribute these spillovers to shared investor bases, sectoral links like crypto mining's energy demand, and regulatory interdependencies. Our evidence confirms that cryptocurrency markets are now integral to global financial stress, transmitting significant volatility to real-economy sectors. This study offers critical insights for investors and policymakers managing risk in an increasingly interconnected financial landscape.
Murugan Ramu, Sneha T, Hemalakshmi V B, K Shwetha · 6 authors
Enterprise Resource Planning (ERP) systems are pivotal in managing organizational workflows across departments such as finance, human resources, inventory, and procurement. However, traditional ERP systems are prone to inefficiencies including lack of transparency, delayed approvals, data inconsistencies, and audit complexities. This paper introduces ChainAutomate-ERP, a blockchain-powered workflow automation framework that integrates smart contracts, reinforcement learning (A3C) agents, and IPFS-based document storage to enhance ERP operations. The proposed SmartFlowChain-Net architecture ensures tamper-proof workflow execution, decentralized approvals, and AI-driven optimization of task routing and resource utilization. A permissioned blockchain network using Hyperledger Fabric is configured with department-wise node identities and inter-channel communication for secure data exchange. Smart contracts autonomously govern workflows such as invoice processing, purchase orders, and leave approvals, eliminating human error and procedural delays. Reinforcement learning agents trained with historical data significantly improved resource allocation and decision efficiency. The framework was benchmarked against traditional ERP systems across five key performance dimensions. Experimental results demonstrate substantial improvements: average workflow execution time reduced by 58.6%, approval accuracy increased from 88.1% (manual) to 98.2% (blockchain-based), and resource allocation efficiency rose from 69.4% to 91.5%. Moreover, audit time dropped by 63.7%, and integrity check failures per 1000 transactions decreased by over 90%. The findings confirm that ChainAutomate-ERP delivers a secure, transparent, and scalable automation layer for next-generation ERP ecosystems. The integration of AI and blockchain technologies positions the system as a transformative solution for enterprises seeking operational excellence and regulatory compliance.
Zero-knowledge proofs (ZKPs) are increasingly adopted in practical cryptographic systems, yet zkSNARK generation remains computationally expensive, limiting scalability. Recent distributed zkSNARK frameworks, such as zkSaaS and Siniel, mitigate this cost by partitioning witnesses across multiple workers. However, they often depend on heavy MPC interactions and full verifier-side proof checking, which hinders their usability in asynchronous or large-scale settings. We present CoVer, a novel distributed zkSNARK system over binary fields, optimized for hardware-level parallelism. CoVer introduces a verifier-guided VOLE-based challenge mechanism that enforces global constraint consistency across subproofs while removing multi-round MPC and tag consistency checks. This design reduces communication and prevents challenge manipulation. Experiments show CoVer achieves up to$150 \times$verification efficiency improvement under variable bandwidth conditions.
Статья рассматривает смарт-контракты как основу цифровой трансформации бизнес-процессов, раскрываются архитектурные принципы, роль в автоматическом исполнении договорных обязательств и повышении прозрачности, а также перспективы интеграции с Интернетом вещей, децентрализованными финансами и цифровыми валютами центральных банков. The article examines smart contracts as a cornerstone of digital business-process transformation, detailing their architectural principles, their role in automating contractual obligations and enhancing transparency, and the prospects for integration with the Internet of Things, decentralized finance, and central-bank digital currencies.
This study examines the design and deployment of scalable blockchain protocols that can serve as the backbone for smart city applications. The manuscript reviews existing blockchain consensus mechanisms—including Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Proof of Authority (PoA)—and evaluates their suitability for heterogeneous smart city ecosystems. It further explores emerging scalability approaches such as sharding, sidechains, directed acyclic graphs (DAGs), and layer-2 protocols, alongside hybrid models that incorporate AI-driven optimization. A comparative simulation-based methodology is employed, assessing throughput, latency, and energy consumption across multiple blockchain prototypes. Results demonstrate that modular hybrid architectures leveraging sharding and DAG structures can increase throughput by up to 400% compared to traditional blockchains, with latency reductions of over 90% and significant energy savings. Beyond technical findings, the study contextualizes blockchain scalability within broader smart city governance frameworks, addressing interoperability between diverse urban domains such as energy microgrids, healthcare data platforms, autonomous mobility systems, and decentralized citizen services. The implications for data privacy, regulatory compliance, and citizen trust are also highlighted, emphasizing the necessity of balancing decentralization with governance oversight. By synthesizing technical, social, and policy considerations, this work contributes a comprehensive roadmap for scalable blockchain adoption in smart cities. Ultimately, the research demonstrates that with careful architectural design and integration of scalability-enhancing techniques, blockchain can evolve from a niche financial tool into a universal urban infrastructure enabler. The findings not only advance blockchain scalability research but also provide actionable insights for policymakers, urban planners, and technologists seeking to design sustainable, citizen-focused smart cities.
This paper addresses one of the most noteworthy issues in the recent virtual asset market, the privacy concerns related to token transactions of Real-World Assets tokens, known as RWA tokens. Following the advent of Bitcoin, the virtual asset market has experienced explosive growth, spawning movements to link real-world assets with virtual assets. However, due to the transparency principle of blockchain technology, the anonymity of traders cannot be guaranteed. In the existing blockchain environment, there have been instances of protecting the privacy of fungible tokens (FTs) using mixer services. Moreover, numerous studies have been conducted to secure the privacy of non-fungible tokens (NFTs). However, due to the unique characteristics of RWA tokens and the limitations of each study, it has been challenging to achieve the goal of anonymity protection effectively. This paper proposes a new token trading platform, the ARTeX, designed to resolve these issues. This platform not only addresses the shortcomings of existing methods but also ensures the anonymity of traders while enhancing safeguards against illegal activities.
Feng Wang, Shuo Yang, Min Zhang, Yang Liu · 6 authors
In decentralized ecosystems, Decentralized Identifiers (DID) and Verifiable Credentials (VC) enable self-sovereign identity, cross-domain interoperability, and privacy-preserving data exchange. However, current VC models face critical limitations, including static attribute binding, inefficient updates, high on-chain verification costs, and privacy leakage. To address these issues, this paper proposes a Dynamic Attribute-oriented Verifiable Credential (DAVC) model, designed to support flexible attribute lifecycle management. The model adopts a three-layer architecture that combines minimal on-chain anchoring, off-chain attribute decoupling, and hierarchical recursive verification to enable efficient and scalable identity verification. First, the onchain layer introduces Sparse Merkle Tree (SMT) root hashes to reduce the need for recording off-chain attribute statuses. Second, the off-chain layer achieves semantic isolation between attributes and identities through an Anonymous Attribute Identifier (AID) mechanism, while improving update efficiency via path caching and incremental strategies. Finally, a Hierarchical Recursive Zero-Knowledge Proof (HR-ZKP) mechanism, based on the Halo2 framework, achieves logarithmic complexity in multiattribute proof generation, supporting attribute-level minimal disclosure and structural anonymization. Experimental results demonstrate that DAVC maintains constant on-chain storage, significantly reduces gas consumption, keeps proof sizes within reasonable limits (e.g., 2.9KB for 10 attributes), and achieves proof generation delays within hundreds of milliseconds. Overall system performance exhibits logarithmic growth as the number of attributes increases. The DAVC model achieves a balance between strong privacy protection, high composability, and dynamic identity expression through minimal on-chain data usage and closed verification paths. This provides a valuable reference for composable identity authentication in Web3 scenarios.