MakerDAO is a decentralized lending protocol providing crypto-backed loans with no intermediaries, backed by volatile assets such as Ethereum (ETH). Loans are liquidated if the value of the collateral dips below a threshold. DeFi compared to traditional finance does not have standardized risk models and default is difficult to model. Earlier models such as Poisson Process and Brownian Motion have the unrealistic premise of constant volatility, which makes them less useful in rapidly fluctuating crypto markets. This paper introduces a Geometric Brownian Motion (GBM) model with rolling volatility to capture real-time market dynamics. The model learns to adapt to prevailing price trends by estimating volatility with a rolling window and enhances the accuracy of default risk estimation. Results indicate that rolling volatility increases the predictive ability of GBM, providing a robust solution to credit risk management in DeFi platforms. The GBM with rolling volatility has 0.006 root mean square error and 0.008 mean absolute error.
Layer 2 rollups offer promising solutions to address Ethereum's scalability issues. However, the centralized nature of the sequencer in these rollups makes them vulnerable to denial of service attacks, in which adversaries overwhelm the sequencer with invalid transactions that cannot be included in blocks, thereby exhausting its computational resources for transaction processing. To mitigate such threat, layer 2 rollups implement the legality check mechanism to filter out invalid transactions before they reach the sequencer.
Zero-knowledge proofs of training (zkPoT) allow a party to prove that a model is trained correctly on a committed dataset without revealing any additional information about the model or the dataset. Existing zkPoT protocols prove the entire training process in zero knowledge; i.e., they prove that the final model was obtained in an iterative fashion starting from the training data and a random seed (and potentially other parameters) and applying the correct algorithm at each iteration. This approach inherently requires the prover to perform work linear to the number of iterations.
In many fields, the need to securely collect and aggregate data from distributed systems is growing. However, designs that rely solely on encrypted data transmission make it difficult to trace malicious users. To address this challenge, we have enhanced the secure aggregation (SA) protocol proposed by Bell et al. (CCS 2020) by introducing verification features that ensure compliance with user inputs and encryption processes while preserving data privacy. We present LZKSA, a quantum-safe secure aggregation system with input verification. LZKSA employs seven zero-knowledge proof (ZKP) protocols based on the Ring Learning with Errors problem, specifically designed for secure aggregation. These protocols verify whether users have correctly used SA keys and their L∞, L2 norms and cosine similarity of data, meet specified constraints, to exclude malicious users from current and future aggregation processes. The specialized ZKPs we propose significantly enhance proof efficiency. In practical federated learning scenarios, our experimental evaluations demonstrate that the proof generation time for L∞ and L2 constraints is reduced to about 10-3 of that required by the current state-of-the-art method, RoFL (S&P 2023), and ACORN (USENIX 2023). For example, the proof generation/verification time of RoFL, ACORN and LZKSA for L∞ is 94s/29.9s, 78.7s/33.9s, and 0.02s/0.0062s for CIFAR10, respectively.
Decentralized Finance (DeFi) has undergone significant expansion, evolving from a niche market into a complex alternative financial ecosystem. This burgeoning landscape now encompasses a diverse array of financial services, including decentralized exchanges, lending and borrowing platforms, stablecoins, derivatives, yield optimization services, prediction markets, and privacy-enhancing technologies such as token mixers. While the total value locked in DeFi protocols—estimated at approximately 77 billion USD—underscores its increasing significance, it simultaneously highlights the critical necessity for robust security measures. This workshop aims to address the pressing security challenges in the maturing DeFi space by convening leading experts from the fields of cryptography, game theory, economics, and cybersecurity. Our primary objective is to foster interdisciplinary dialogue and showcase cutting-edge research that rigorously examines the current state of DeFi security and charts a comprehensive path forward. The anticipated outcomes include a prioritized research agenda, new collaborative initiatives bridging theoretical advancements with practical implementations, and a strategic roadmap for enhancing security in the rapidly evolving DeFi ecosystem. This year's program features a keynote talk by Prof. Vassilis Zikas, two invited talks by the winners of the Best DeFi Paper Award (theoretical research track and applied research track), and four presentations of accepted original papers, showcasing both fundamental advances and real-world applications.
Mohit Malik, Rahul S Mor, Vijay Kumar Gahlawat, Vikas Kumar
• Presents a novel hybrid blockchain and AI-enabled end-to-end SC traceability model. • Validates a multilayer Web3-based architecture integrating smart contracts, ML algorithms & IoT-enabled data capture. • Offers a proof-of-concept and feasibility analysis, highlighting scalability, transaction speed & system responsiveness. Conventional traceability systems without real-time information transmission are susceptible to tampering. In contrast, blockchain and artificial intelligence (AI)-enabled traceability models offer transparency and accountability, given their decentralized nature and immutability. This research conceptualizes and develops a hybrid blockchain and AI-enabled traceability (prototype) model and implements it in the dairy industry. The study includes a collaborative research methodology, including a literature review to analyze the existing traceability solutions, identify data entry points, select model requirements, and deploy smart contracts, decentralized applications (Dapps) and Web3 technologies to develop and validate the proposed model via Testnet . The findings present the user interface developed as a prototype traceability model and its characteristics, such as transparency, decentralized nature, and immutability, followed by practical validation. The post-implementation data analysis highlighted the security, privacy, smart contract validation rules, and comparative insights, as well as the alignment of the theoretical model with practical applications using Web3 technologies. This research contributes to the literature on hybrid blockchain and AI-enabled traceability, highlighting the potential for exploring opportunities in the food industry.
This project applies the latest cutting-edge Web3 technologies to design a Blockchain-Based Proof of Contribution (PoC) Framework aimed at improving efficiency, security, and transparency in managing research outputs and safeguarding intellectual property in collaborative settings. This framework uses the features of smart contracts on Ethereum and blockchain technology to guarantee transparency and accountability by automating the process involved in awarding the funds raised, verification of donations, and resolving disputes. After hashing a contribution cryptographically, every one of them stays on the blockchain, generating an irreversible record of its veracity. The application of IPFS in this makes decentralized and safe offchain storage possible, scalable, and intact. It has an automated dispute resolution process, which guarantees that the outcomes will be fair and not sent out for arbitration from outside. The framework deals with issues of trust, data security, and intellectual property in order to provide a safe and effective setting in which researchers can focus on creativity, knowing their work is safely documented, appropriately acknowledged, and protected from misuse.
Muh. Rapi, Muh. Hanif, Ghifary Duyufur Rohman, Mukhtar Arif Siraj
Purpose This paper aims to review the perspectives and views of modern scholars on decentralized finance (DeFi) and assess the alignment of DeFi platforms and concepts with fundamental Islamic principles. The goal is to ensure that DeFi adapts to Islamic principles, maintaining the relevance of maqasid syariah within the evolving financial technology landscape. Design/methodology/approach This research uses a qualitative methodology using sentiment analysis to evaluate 133 academic articles retrieved from the Scopus database. Sentiment extraction and analysis were performed using ATLAS.ti software to analyze the scholarly discourse on DeFi from an Islamic perspective. Findings The findings of this study indicate that 59% of the studied texts exhibit positive sentiments toward DeFi, reflecting academic confidence in its potential advancements and benefits for the financial sector. Neutral sentiments constitute 25% of the responses, acknowledging both the opportunities and challenges DeFi poses. In contrast, 16% of the texts express negative views, emphasizing the need for a cautious approach to their broader implications. From an Islamic perspective, DeFi is generally permissible under the fiqh muamalah principle, provided it adheres to Sharia principles. This study suggests that implementing DeFi in financial institutions can enhance the equitable distribution of funds and resources, increase transparency and reduce transaction costs, thereby improving community welfare and economic inclusion. However, challenges in regulation and risk management necessitate cooperation among key players. Research limitations/implications This research is significant, as it offers a foundational understanding of DeFi’s potential within Islamic finance, guiding future innovations and regulatory frameworks in this emerging field. This study highlights the importance of aligning DeFi developments with Islamic principles to foster equitable and inclusive financial growth while addressing regulatory and risk management challenges. Originality/value To the best of the authors’ knowledge, this research is the first study to conduct sentiment analysis of the scientific literature on DeFi from an Islamic perspective, providing a novel viewpoint on the intersection of DeFi and Islamic principles.
M. Zeeshan Haider, Tayyaba Noreen, M. D. Assuncao, Kaiwen Zhang
Sharding has emerged as a key technique to address blockchain scalability by partitioning the ledger into multiple shards that process transactions in parallel. Although this approach improves throughput, static or heuristic shard allocation often leads to workload skew, congestion, and excessive cross-shard communication diminishing the scalability benefits of sharding. To overcome these challenges, we propose the Predictive Shard Allocation Protocol (PSAP), a dynamic and intelligent allocation framework that proactively assigns accounts and transactions to shards based on workload forecasts. PSAP integrates a Temporal Workload Forecasting (TWF) model with a safety-constrained reinforcement learning (Safe-PPO) controller, jointly enabling multi-block-ahead prediction and adaptive shard reconfiguration. The protocol enforces deterministic inference across validators through a synchronized quantized runtime and a safety gate that limits stake concentration, migration gas, and utilization thresholds. By anticipating hotspot formation and executing bounded, atomic migrations, PSAP achieves stable load balance while preserving Byzantine safety. Experimental evaluation on heterogeneous datasets, including Ethereum, NEAR, and Hyperledger Fabric mapped via address-clustering heuristics, demonstrates up to 2x throughput improvement, 35\% lower latency, and 20\% reduced cross-shard overhead compared to existing dynamic sharding baselines. These results confirm that predictive, deterministic, and security-aware shard allocation is a promising direction for next-generation scalable blockchain systems.
In contemporary art, the question of organization, and of how to organize differently, has become a key concern, leading to a wide range of organizational experiments as material of art itself. In this paper, we investigate contemporary art’s organizational aesthetics by turning to the exhibition documenta fifteen as the hitherto most visible forum of art’s turn to alternative organization. We identify four modes of organizing – diagramming, hanging out, infrastructuring and archiving – performed and exhibited at the exhibition, and we develop the notion of pre-enactment as aesthetic register of reimagining and rehearsing organization. We then outline an understanding of organizational eutopianism as atmospheric, chronopolitical and resistant to assumptions of frictionless scaling and circulation
The article proposes a comprehensive approach to solving the data protection problem in decentralized distributed information storage systems based on blockchain technology. A conceptual "SecureChain" model has been developed that integrates modern cryptographic protection methods with programmable smart contract logic for automated access management and data integrity assurance. The model employs a multi-level architecture including data layer, smart contract layer, network interaction layer, and user interface. The key innovation is the application of Shamir's threshold encryption schemes (t, n) controlled by smart contracts, combination of symmetric and asymmetric encryption algorithms (AES-256 for data, Curve25519 for keys), and implementation of a "secret disclosure" mechanism to enhance critical information security. Experimental validation of the model was conducted in three typical use cases: medical data storage system, corporate document management system, and electronic voting platform. Results demonstrate significant improvement in key security metrics compared to traditional approaches: resistance to attacks on individual nodes (by 65%), data confidentiality (by 72%), access audit capability (by 90%), and fault tolerance (by 58%) with moderate increases in storage costs (by 15%) and access time (by 10%). Additional scalability analysis showed a near-linear relationship between performance and both data volume and number of users. The proposed model and implementation methodology have significant practical value for organizations working with confidential data and requiring reliable distributed storage systems that meet modern security requirements and regulatory standards. Keywords: smart contracts, cryptographic protection, threshold encryption, distributed systems, key management, data integrity, network security.
The rapid evolution of financial technologies (FinTech) and digital assets—including cryptocurrencies, decentralized finance (DeFi), and tokenized capital markets—has created an unprecedented need for secure, scalable, and computationally efficient systems. This study examines the transformative potential of quantum computing in reshaping financial technology infrastructures and digital asset ecosystems. Traditional computational models, constrained by classical encryption limits and the exponential growth of financial data, face increasing inefficiencies in handling real-time risk assessment, portfolio optimization, and transaction verification. Quantum computing, with its capacity for superposition, entanglement, and parallel state evaluation, provides novel opportunities to redefine data security, financial modeling, and cryptographic mechanisms in the digital economy. The research explores how quantum algorithms—notably Quantum Approximate Optimization Algorithm (QAOA), Quantum Fourier Transform (QFT), and Grover’s search algorithm—can enhance financial operations such as market forecasting, fraud detection, and asset pricing. Additionally, it investigates quantum-resistant cryptography to safeguard digital asset networks against the vulnerabilities introduced by future quantum decryption capabilities. By integrating hybrid quantum–classical frameworks, this approach enables the development of sustainable, adaptive, and transparent financial systems. The findings highlight quantum computing’s potential to advance financial inclusion, increase transaction speed, and improve systemic resilience. As global financial markets transition toward quantum readiness, the convergence of FinTech and quantum innovation is expected to redefine how digital assets are managed, traded, and secured—marking a paradigm shift toward quantum financial intelligence.
Purpose Cement manufacturing is a vital yet emission-intensive industry that faces challenges in certifying sustainable production practices, driven by the need for transparency, accountability and regulatory compliance. This paper provides a blockchain-based certification framework to enhance traceability and sustainability in cement production. Design/methodology/approach Leveraging Ethereum smart contracts (SCs) and blockchain technology, our solution ensures decentralized, immutable tracking of emissions data, production processes and compliance through secure interactions among regulators, manufacturers and auditors. The framework facilitates deployment, registration, reporting, auditing and certification. Detailed insights into system architecture, algorithms, SC implementation and validation are provided. Security analysis evaluates access controls, data privacy and vulnerability mitigation, while cost analysis highlights the framework's economic feasibility by examining gas costs for key functions. Findings The blockchain-based framework successfully produced a scalable solution with a modular design to allow for flexible deployment by different regulatory bodies. Transparency was achieved through blockchain events announcement, and accuracy was ensured through periodic auditing rounds. Security analysis results show no serious vulnerabilities. The developed framework proves a cost-effective solution for certifying sustainable production practices in the cement industry, with potential applications across other heavy manufacturing sectors. Research limitations/implications Cement manufacturing is a vital yet emission-intensive industry that faces significant challenges in certifying sustainable production practices, driven by the need for transparency, accountability and regulatory compliance. Practical implications We believe that this paper provides the following practical implications: Innovative Framework: A blockchain-based certification system promoting adherence to sustainable processes in cement manufacturing. SC Implementation: Detailed insights into the system architecture, implementation and validation of algorithms and SCs. Comprehensive Analysis: A thorough security analysis of SC coding and a cost analysis highlighting the economic feasibility of our proposed framework. Social implications The proposed methodology will help all stakeholder involved in the cement production and supply chain have a better understanding and a robust tool to observe and learn about operations, tasks and other activities made through sustainable cement production Originality/value The study contributes a novel blockchain-based method to certify sustainable production in energy-intensive industries, especially cement, offering a valuable tool that ensures transparency and immutability.
Rabimba Karanjai, Yang Lu, Lei Xu, Weidong Larry Shi
Generative AI and modern Machine Learning are rapidly transforming industries, creating new tools and use cases. As these models grow, there is an increasing need for reliable Machine Learning Operations (MLOps) to manage and deploy them. At the same time, demand is rising for smarter smart contracts and more complex on-chain computation. Our paper presents a new framework that brings GenAI inference directly onto the blockchain. Using the Cosmos SDK, Ethermint, and the ONNX runtime, we enable AI models to run on-chain across multiple blockchain nodes. We evaluate the system’s feasibility, performance, and portability, showing that it can support different AI engines and model types. By enabling GenAI inference on-chain, our approach expands what smart contracts can do, especially for data-driven DeFi, and opens the door to new applications in the future.
The exponential growth of digital finance—encompassing online banking, digital assets, decentralized finance (DeFi), and algorithmic trading—has intensified the need for robust cybersecurity frameworks. However, the rise of quantum computing presents a dual challenge: while it enables revolutionary advances in data analytics and optimization, it simultaneously threatens the cryptographic foundations of contemporary financial systems. This research explores the emerging field of quantum cybersecurity and its implications for safeguarding financial infrastructures in the post-quantum era. Traditional encryption methods such as RSA, ECC, and Diffie–Hellman key exchange are vulnerable to quantum attacks, particularly through Shor’s algorithm and Grover’s search algorithm, which can efficiently break asymmetric and symmetric cryptographic schemes. The study evaluates quantum-resistant cryptographic protocols—including lattice-based, hash-based, and multivariate polynomial encryption—as viable solutions for ensuring financial data integrity, transaction confidentiality, and regulatory compliance in quantum-vulnerable environments. Furthermore, it investigates Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as hardware-assisted techniques for achieving unconditional security in financial communications and transaction authentication. By integrating quantum cryptography, hybrid encryption, and AI-driven threat modeling, this work outlines a roadmap for financial institutions transitioning toward quantum-secure infrastructures. The findings demonstrate that quantum cybersecurity is not merely a defensive measure but a transformative enabler for resilient digital finance, aligning with global efforts to achieve technological sovereignty, financial stability, and sustainable innovation in the era of quantum computing.
Subject. The article discusses innovative digital technologies in improving the effectiveness of coordination of economic entities. Objectives. Based on the theory of transaction costs, the new institutional economics and the theory of public choice, the study aims to identify specifics of the impact of distributed ledger technologies on economic institutions, their transparency, and effective coordination in economic systems. Methods. We employed methods of theoretical analysis of literature on the new institutional economics, the theory of public choice, and constitutional economics. Innovative digital technologies are considered from the perspective of transaction cost reduction. Results. The paper shows that innovative digital technologies are not limited to a narrow application in the field of digital currencies and smart contracts, but represent a fundamental institutional mechanism that helps reduce transaction costs, increase transparency of transactions, and form more perfect market orders. Special attention is paid to the role of distributed registry systems in spontaneous and polycentric management according to E. Ostrom, and in "constitutional choice" according to J. Buchanan. Conclusions. The decentralized nature of distributed ledgers, backed by cryptographic methods and economic incentives, has the potential to transform organizational and political institutions, stimulating increased trust and inclusivity in collective decision-making processes.
Smart contracts automate blockchain transactions but are vulnerable to reentrancy attacks, where an attacker repeatedly calls a function before the contract updates its state, stealing funds. A well-known case is the 2016 DAO exploit, which caused a loss of $60 million.This study investigates methods to protect contracts from such attacks through a literature review, a case study comparing a vulnerable and a fixed contract, and evaluation of analysis tools (Mythril, Slither, Securify, Sereum, BlockWatchdog).The expected results include identifying effective coding patterns (like checks-effects-interactions and reentrancy guards), assessing tool accuracy, and providing secure development guidelines. All experiments will be conducted safely on test networks.
Artificial intelligence (AI) is altering the systemic risk topology of decentralized finance (DeFi). While prior studies examine vulnerabilities in each domain, their intersection remains undertheorized. This paper frames the AI-DeFi ecosystem as a complex adaptive system (CAS) to identify three mechanisms by which AI reshapes systemic risk: algorithmic homogeneity, which synchronizes agent responses; dynamic network rewiring, which amplifies structural fragility; and emergent multi-agent behavior, which produces contagion without centralized intent. Together, these forces introduce endogenous, path-dependent failure modes opaque to reductionist analysis. This framework provides a foundation for empirical analysis and anticipatory governance in AI-mediated financial systems.
Adoption of DeFi, Central Bank Digital Currency (CBDC), and Tokenized Multiassets necessitates new security architectures for Regulated Tokenized Multiasset Networks (RTMNs). Traditional approaches are insufficient for the distributed nature of decentralized finance, and Zero Trust models face compliance and efficiency challenges in financial networks. This paper proposes a novel framework for diverse use cases, guarantees composability, atomicity, settlement finality, and enforced compartmentalization, sharing minimal necessary information, enabling privacy-by-design. A detailed analysis of the framework’s application in RTMNs is presented, evaluating its characteristics in the context of tokenizing government securities.