Longbo Han, Xiaodong Li, Lin You, Gengran Hu · 8 authors
Vehicular ad-hoc networks (VANETs) require authentication mechanisms that simultaneously deliver privacy, accountability, and timely cross-domain synchronization. The existing schemes struggle to balance unlinkable anonymity with effective tracing. They are also vulnerable to future quantum adversaries and rely on slow and costly revocation workflows. We present ZebraCPA, a decentralized conditional privacy-preserving authentication (CPPA) framework that combines lattice-based traceable ring signatures (TRS) with zero-knowledge (ZK) proofs and a consortium blockchain. Our TRS design removes linkability tags and embeds a tracing trapdoor only recoverable by the authorized auditors. It naturally extends to threshold tracing for multi-auditor settings. To avoid the plain-text key escrow, ZebraCPA leverages the additively homomorphic property of the commitments to support the ciphertext-only key updates by the vehicles, preventing the catastrophic key leakage at authorities. A hierarchical blockchain layer provides fast, consistent synchronization of active-key status across regions. The experiments show 1.7×–7.0× speedups over state-of-the-art baselines in signing/verification while retaining an anonymity-set size of N=10. The network-level simulations further indicate that ZebraCPA reduces an average packet delay by 30.7% - 61.6% compared with the baselines under moderate traffic densities. Moreover, the security of ZebraCPA is validated through our informal analysis under the Dolev-Yao model. Overall, ZebraCPA achieves post-quantum security, strong anonymity with conditional traceability, and practical deployment efficiency for VANETs, outperforming the existing solutions in terms of both latency and robustness.
The built environment is a critical frontier for climate change mitigation and adaptation, with residential buildings accounting for a substantial portion of global energy consumption and greenhouse gas emissions. This paper presents a critical review of contemporary literature (2020-2025) synthesizing advancements in climate-resilient housing through integrated architectural and renewable energy solutions. A systematic analysis of 51 studies examines three core areas: passive and active architectural design for thermal resilience; the role of decentralized renewable energy in enhancing autonomy; and the socio-technical, policy, and governance dimensions of implementation. The present review identifies a paradigm shift from static efficiency toward dynamic, adaptive building systems, highlighting the efficacy of bioclimatic design, smart materials, and AI-driven management. Decentralized solar energy is underscored as fundamental for decarbonization and energy security, though its success depends on supportive policies, community engagement, and equitable finance. Persistent gaps are noted, including the need for holistic lifecycle assessments, scalable models for low-income contexts, and stronger integration of technical and social equity approaches. The review concludes by advocating for a transformative shift toward adaptive, regenerative, and just residential environments.
Contemporary digital currency systems face fundamental challenges in achieving optimal balance between transaction privacy, computational efficiency, and cryptographic security. While zero-knowledge proof systems have dominated privacy-preserving cryptocurrency research, their practical implementations often involve prohibitive computational overhead that limits real-world deployment. This paper presents a comprehensive analysis of the Elliptic Homomorphic Token (EHT) protocol, which leverages elliptic curve-based partially homomorphic encryption combined with parallel processing architecture to enable privacy-preserving peer-to-peer transactions without the computational complexity of zero-knowledge constructions. Our theoretical analysis demonstrates strong privacy guarantees under standard cryptographic assumptions, while experimental evaluation shows that EHT achieves 500,000 transactions per second with parallel processing and 50-100ms latency. The protocol eliminates the need for complex zero-knowledge proofs by directly utilizing elliptic curve cryptographic primitives, resulting in performance improvements exceeding 1000× over existing privacy-focused systems while maintaining equivalent security properties through formally proven cryptographic guarantees.
Hari Purnama, I Putu Bakta Hari Sudewa, Tazkia Nizami, Bagas Sambega Rosyada · 6 authors
Electronic Medical Records (EMRs) are mandatory in Indonesia following the Ministry of Health regulation, which raises significant challenges in data security and patient-centric access control. Current implementations rely on centralized healthcare systems or third-party vendors, creating risks of unauthorized access, data leakage, and uncertain data integrity. To address these issues, this study proposes DecMed, a decentralized EMR management framework built on IOTA Distributed Ledger Technology (DLT). DecMed integrates Capability-Based Access Control (CapBAC), Proxy Re-Encryption (PRE), and the InterPlanetary File System (IPFS) to enforce patient ownership of medical data. Patients actively grant or revoke access, define access duration, and selectively share data with healthcare personnel. The system is implemented using smart contracts in the Move programming language on the IOTA ledger, while encrypted clinical data is stored on IPFS. Evaluation through unit testing of various unauthorized access scenarios demonstrates that DecMed effectively enforces fine-grained access rules, preserves data confidentiality and integrity, and ensures compliance with national healthcare requirements.
Hamid Najafi Bouyaghchi, Ameneh Farahani, Ismail A Mageed
The cryptocurrency market is volatile, which makes it very difficult to accurately predict. The Long Short-Term Memory (LSTM) is an approach to Predict Price Cryptocurrency (PPC) that uses price time series data. However, in this method, the prediction accuracy is dependent on the tuning of meta-parameters. Therefore, to tune these meta-parameters, an improved version of the optimization algorithms is needed that provides the task of selecting the optimal values of these parameters for price predictions. Therefore, in this study, the LSTM is combined with the classic version of the Differential Evolution (DE) algorithm, and the real data against the prediction results of the model presented in this study showed the appropriate accuracy of this model. Then, the classic version of the DE algorithm was modified to reduce its errors compared to previous algorithms. In this regard, coding was done in MATLAB version 2023b software, and the improved version was compared in terms of error rate with the Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and the new Bald Eagle Search (BES) algorithm, which showed an accuracy of 86.94% for the improved model in this study.
The convergence of social networking and electronic commerce has given rise to the social e-commerce paradigm, where content creators serve as the primary drivers of consumer engagement and purchase decisions. However, this ecosystem faces a critical tension between the need for high-precision ad targeting to sustain monetization and the increasingly stringent requirements for user privacy preservation. Traditional centralized recommendation systems require the aggregation of massive user behavioral datasets, creating significant risks of data leakage and violating emerging regulatory frameworks. To address this challenge, we propose a novel framework titled Fed-ZKC (Federated Zero-Knowledge Creator). This architecture synergizes Federated Learning (FL) with Zero-Knowledge Proofs (ZKP) to enable privacy-preserving ad targeting while ensuring verifiable monetization attribution for creators. In our system, user preference models are trained locally on edge devices to prevent raw data transmission, while a cryptographic verification layer ensures that ad interactions are genuine without revealing user identities to the platform or the creators. Extensive experiments conducted on large-scale real-world datasets demonstrate that Fed-ZKC achieves recommendation accuracy comparable to centralized baselines while reducing privacy leakage risks by orders of magnitude. Furthermore, the implementation of succinct non-interactive arguments of knowledge (zk-SNARKs) introduces minimal computational overhead, making the protocol feasible for deployment on modern mobile processors.
Distributed Energy Resources (DER) such as solar PV, wind micro-turbines, smart inverters, electric vehicles (EVs), and home energy storage systems are rapidly increasing in modern power systems. However, their decentralized nature introduces complexities in coordination, demand–supply balancing, and resilience. Existing blockchain-based DER frameworks primarily focus on peer-to-peer (P2P) trading, security, and certificate validation, but lack mechanisms for coordinated swarm-like behaviour among DER units. This paper introduces a novel concept—Blockchain-Based Swarm Coordination of DER Clusters, inspired by swarm intelligence principles such as self-organization, collaboration, local decision-making, and emergent global behaviour. The proposed system integrates blockchain, multi-agent coordination, and decentralized smart contracts to enable secure, autonomous, and scalable coordination of DER clusters. A layered architecture, cluster formation mechanism, consensus-driven decisioning, and energy-sharing algorithms are presented. The framework significantly enhances grid flexibility, improves energy distribution efficiency, reduces central-dependency, and enables real-time proactive response during grid fluctuations. Simulation-driven conceptual outcomes demonstrate improved DER responsiveness, fault tolerance, trust, and transparency. This work establishes a new research direction by merging blockchain with swarm intelligence for next-generation decentralized energy systems.
We propose Trustless Agent Swarms, a framework enabling privacy-preserving coordination among autonomous AI agents on EVM-compatible blockchains. Our system integrates four cryptographic primitives: (1) Groth16 zero-knowledge proofs for proving reputation thresholds without revealing scores; (2) EIP-5564 stealth addresses for unlinkable fund transfers; (3) ERC-4337 account abstraction for gasless autonomous execution; and (4) Semaphore for anonymous group signaling. We implement a 586-constraint reputation proof circuit and deploy five smart contracts on Base Sepolia. Proof generation: 580ms. On-chain verification: 407,576 gas.
Materi ini membahas aspek praktis investasi dan trading aset kripto dengan pendekatan literasi risiko dan kehati-hatian, khususnya untuk membantu peserta memahami bahwa kripto pada dasarnya merupakan aset, sehingga interaksinya harus dianalisis sebagaimana interaksi pada ekosistem aset pada umumnya. Pembahasan dimulai dari kerangka besar ekosistem kripto yang menempatkan pengguna (aktor), platform (venue), dan aset (goods) sebagai tiga elemen utama pembentuk risiko, kemudian dilanjutkan dengan pengenalan spektrum aset kripto (coin, token, dan NFT), mekanisme kustodi (custodial vs non-custodial), serta pentingnya pengamanan private key dan seed phrase. Materi juga menguraikan mekanisme transaksi pada pasar spot di CEX (order book) dan DEX (AMM), termasuk peran liquidity provider, arbitrage, slippage, dan risiko likuiditas. Selain itu, dijelaskan berbagai aktivitas dalam ekosistem Web3 seperti staking, lending-borrowing DeFi, strategi long/short berbasis jaminan, hingga bahaya derivatif dan leverage yang dapat memicu likuidasi cepat. Selanjutnya, materi menekankan pentingnya analisis fundamental (tokenomics, aktivitas developer, metrik finansial, dan data on-chain), serta memberikan pengantar mengenai aset dunia nyata yang ditokenisasi (RWA), metaverse, dan NFT beserta parameter evaluasinya (provenance, kolektibilitas, utilitas, finansial, roadmap, dan komunitas). Pada sisi mitigasi risiko, materi memetakan bahaya utama di ekosistem kripto—mulai dari risiko CEX, token, DeFi, hingga interaksi sosial (phishing, social engineering, pig butchering, dan FOMO)—serta menawarkan kerangka due diligence 6D (Define, Document, Diversify, Detect, Defend, Discipline) sebagai panduan pengambilan keputusan yang lebih rasional. Kesimpulan utama materi ini menegaskan bahwa risiko utama dalam kripto bukan hanya terletak pada instrumennya, tetapi juga pada kualitas riset, pengendalian diri, dan kemampuan menjaga diri pengguna saat berinteraksi dengan ekosistem digital.
ABSTRACT AI‐driven personalization now structures search, recommendation, pricing, and service across the consumer journey, heightening a core dilemma: maximizing relevance and efficiency without compromising autonomy and trust. This article advances a capability‐based account of responsible personalization. I theorize that technology sense‐breaking (challenging legacy assumptions) and sense‐giving (constructing shared meanings) foster strategic flexibility, which, in turn, enables two outcomes: (a) product/process innovation performance and (b) consumer‐facing safeguards that calibrate trust—transparent AI disclosure, adjustable recommendation intensity, and human‐override/redress mechanisms. I further argue that transformational leadership amplifies the translation of sensemaking into flexibility, steering reconfiguration toward “engagement without coercion.” A firm‐level, multi‐respondent survey of Taiwan‐based organizations adopting AI/Web3 in marketing and service contexts is used to test a moderated‐mediation model with validated multi‐item measures and PLS‐SEM, alongside power checks, CMV diagnostics, and robustness analyses. By endogenizing UX governance within organizational capabilities and leadership, the study links internal reconfiguration to external consumer dignity, specifying when firms are most likely to implement autonomy‐preserving designs. The contribution is a precise, operational blueprint for aligning market performance with ethical experience through capability formation and trust calibration
Abstract As artificial intelligence (AI) systems become increasingly integrated into critical applications, ensuring trust in their outputs has emerged as a central challenge. Verifiable machine learning (ML) is one approach to addressing this challenge, providing guarantees that results are both correct and reproducible. Existing paradigms, however, provide only partial solutions: zero-knowledge ML (ZKML) achieves strong cryptographic assurances but suffers from limited scalability and high resource costs, while optimistic ML (OPML) supports a wider range of models but relies on economic incentives and long dispute periods. In this work, we propose zk-OPML, a novel hybrid framework that integrates optimistic verification with zero-knowledge proofs (ZKPs). The approach decomposes ML inference into operator-level computations, selectively generating ZKPs for isolated ONNX operators, while retaining the scalability of the optimistic paradigm. We present a prototype implementation and evaluate its performance by benchmarking it against ZKML and OPML. Our results show that zk-OPML achieves faster verification for more complex inference tasks and scales more effectively to larger models, while avoiding the excessive costs of end-to-end ZKML. The modular design of zk-OPML further enables future extensions with the latest advances in the field of ZK.
Open access
Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
In the Web3 ecosystem, "dead assets" such as micro-token fractions (dust) and abandoned low-value NFTs continue to accumulate on networks, losing liquidity due to transaction fee constraints. This paper proposes the concept of the "Scavenger Protocol," which leverages the object-oriented architecture and parallel processing capabilities of the Sui blockchain to automatically detect, melt down, and recirculate these dormant assets back into the market as reusable resources (Scrap tokens). By implementing a smart contract-based mechanism where "abandoned value is automatically shuffled" without the need for central administrators, we aim to achieve the autonomous revitalization of the on-chain economy. Charitable Pledge: In the event that the implementation of this protocol generates any personal revenue or financial returns for the author, 60% of those proceeds will be donated to charitable causes. This commitment aligns with the Web3 ethos of supporting public goods and giving back to society. Disclaimer: This paper is for informational, academic, and conceptual purposes only and does not constitute financial, investment, legal, or regulatory advice. The "Scavenger Protocol" presented herein is a theoretical framework and thought experiment. The authors do not guarantee the realization of this protocol, its technical feasibility, or any financial returns. Any future implementation of this protocol by third parties must be executed strictly in compliance with applicable local and international laws. The authors assume no liability or responsibility for any damages, legal disputes, or financial losses arising from the use, interpretation, or implementation of the concepts described in this document. Open Design and Protocol Revenue Model (Tokenomics) This protocol is designed as a universal foundational primitive, unconstrained by specific applications or use cases. Other developers and projects are encouraged to freely fork, adapt, and build upon this mechanism to construct systems with their own customized rules. To ensure the system's long-term sustainability, we define a minimal baseline rule: the system automatically collects a flat 1% protocol fee during operations—specifically at the time of melting down dormant assets and generating new ones. The revenue generated from this 1% fee is directly applied to the "Charitable Pledge" outlined above. This creates the financial engine where 60% of the proceeds are directed toward social givebacks and donations, while the remaining 40% is allocated to protocol maintenance and the founder.
Financial assets are central to economic stability, yet the macroeconomic sensitivity and predictability of emerging digital assets, particularly non-fungible tokens (NFTs), remain unclear. This study evaluates the responsiveness of NFTs, cryptocurrencies, and traditional assets to interest rate and inflation fluctuations using time series forecasting and sensitivity analysis. ARIMAX, Partial Least Squares, Ridge Regression, and Long Short-Term Memory (LSTM) models are employed to capture linear and nonlinear dynamics across asset classes. Using daily data from July 2017 to November 2024, results indicate that LSTM achieves superior predictive accuracy for highly volatile and nonlinear assets, although forecast reliability is limited by structural breaks and thin trading. Traditional assets such as bonds and gold display stable sensitivities to macroeconomic variables, reinforcing their hedging role. In contrast, digital assets exhibit higher volatility and weaker, less stable macroeconomic linkages. NFTs show low correlations with traditional assets, suggesting diversification potential, but low forecast error variance does not imply low risk. Cryptocurrencies demonstrate stronger macroeconomic sensitivity alongside greater instability. Overall, the findings reveal a trade-off between diversification benefits and forecast reliability when integrating digital assets into portfolios.
Byzantine Fault Tolerant (BFT) consensus forms the foundation of many modern blockchains striving for both high throughput and low latency. A growing bottleneck is transaction execution and validation on the critical path of consensus, which has led to modular decoupled designs that separate ordering from execution: Consensus orders only metadata, while transactions are executed and validated concurrently. While this approach improves performance, it can leave invalid transactions in the ledger, increasing storage costs and enabling new forms of strategic behavior. We present the first systematic study of this setting, providing a formal framework to reason about the interaction between consensus and execution. Using this framework, we show that the decoupled design enables a previously unidentified attack, which we term gaslighting. We prove a fundamental trade-off between resilience to this attack and resource capacity utilization, where both are impossible to achieve deterministically in the decoupled model. To address this trade-off, we discuss an intermediate model for leader-based protocols that is robust to gaslighting attacks while achieving high throughput and low latency.
Coroutines, as an abstract programming construct, are a generalization of functions that can suspend execution part- way for later resumption. Coroutine Types are behavioral types to model interactions of coroutines with a single receiving operation followed by a single yielding operation. Coroutine Types have been applied to model-driven engineering, smart contracts, and test case generation. We contribute a Flow extension to Coroutine Types, so that coroutines with more than one receiving and yielding operation can be modeled. We accordingly revise the reduction rules of Coroutine Types. To show the usefulness of the Flow extension, we contribute a type system that maps expressions of the Go programming language to Coroutine Types. If the reduction result is 0, the two channel operations are paired properly and the program has no deadlocks. We choose Go because it is a popular programming language for distributed systems, but a frequent kind of bugs in Go is deadlocks due to the wrong use of concurrency features. We concentrate on the most commonly used semantics in Go: unbuffered channels with the keywords go and defer. Our Flow extension and the type system recognize 17 patterns of channels and goroutine interactions, including mismatched receivers and senders, nested goroutines, etc. We also integrate the Z3 SMT solver to take account of conditional execution and type inheritance. Other static or dynamic deadlock detectors crashed or gave wrong predictions in some patterns. Therefore, our type-based deadlock analyzer not only fills the gap in the landscape of value-based detection, but also complements existing detectors.
This paper presents the completed Y.I.N. Mazari Architecture in its final 20-layer form, addressing two compounding failures in AI governance: the verification paradox where organizations cannot prove compliance without trusting their own infrastructure, and the platform determinism gap where AI inference produces different results across hardware architectures. The architecture integrates five physics foundation layers establishing energy-anchored provenance through Landauer limit accounting, domain routing, measurement precision, blockchain anchoring, and deterministic parity verification using Residue Number System arithmetic. Core governance layers enforce constitutional constraints through cryptographic authorization, differential privacy, and multi-party verification. Advanced layers provide zero-knowledge proofs, immutable audit trails, automated regulatory reporting, quantum resistance, and meta-governance oversight. Layer 0E, the Deterministic Parity Engine introduced in this final architecture, achieves bit-exact cross-platform computational reproducibility, enabling independent verification of AI operations by any party on any hardware. Combined with Layer 14, SENTINEL independent verification, the architecture produces governance evidence that no party can forge, no party can suppress, and any party can reproduce independently on arbitrary hardware. The complete 20-layer stack addresses GDPR Article 5, DORA Article 28, EU AI Act Article 50, HIPAA Security Rule, and provides 30-year quantum-resistant durability through NIST FIPS 203 post-quantum cryptography. Patent portfolio: 27 USPTO applications covering the architecture, priority November 23, 2025. The name Y.I.N. honors Yanis, Ilyan, and Neylia Mazari, representing the principle: Your Information Never leaves your control.
The next-generation e-health systems, which include electronic health records (EHRs), telemedicine platforms, and Internet of Medical Things (IoMT) environments, need a strong access control system that protects sensitive medical data while maintaining user privacy. The conventional access control systems face security risks because of credential theft, spoofing attacks, and their reliance on centralized trust, and their inability to scale. Blockchain-enabled multimodal biometric authentication provides a secure and decentralized solution for access control in e-health systems, according to current technological advancements. This paper provides an extensive assessment of blockchain-based multimodal biometric authentication systems, which deliver privacy-protecting access control solutions for future e-health systems. The review further examines central techniques for protecting biometric templates, zero-knowledge proofs, homomorphic encryption, and secure off-chain storage systems. The research assessed existing methods by comparing efficiency for access control, ability to protect user data, capacity to handle growing user needs, ability to work with other systems, and compliance with the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) regulations. The research identifies open challenges that need resolution, which include biometric data revocability, latency constraints, cross-platform interoperability, and limited real-world deployments. The study presents upcoming research paths that will investigate lightweight blockchain systems, post-quantum cryptography, cross-chain medical identity management, and adaptive access control systems in extensive e-health environments. The review demonstrates that blockchain-based multimodal biometric authentication serves as a suitable foundation that enables secure access control through decentralized systems that protect user privacy in upcoming e-health technologies.
This research is motivated by the rapid development of blockchain technology and the increasing use of smart contracts in modern business transactions in Indonesia, while the national legal framework does not yet provide regulations that comprehensively regulate the validity, automatic execution mechanism, and legal accountability of smart contracts. The absence of clear technical rules raises various problems, especially related to the suitability of smart contracts with the legal terms of the agreement in the Civil Code, ranging from the aspects of the agreement, the competence of the parties, certain objects, and halal causa. To answer these questions. This study uses a normative juridical method with legislative and conceptual approaches. Various regulations, including the Civil Code, ITE Law, PP 71/2019, and POJK 77/2016, as well as provisions governing electronic systems and transactions, were analyzed to assess the extent to which smart contracts can be recognized in the Indonesian legal system. The results show that although smart contracts can be positioned as legitimate agreements based on the principle of freedom of contract and the open nature of Indonesian contract law, there are still significant regulatory loopholes that have the potential to create legal uncertainty. The main challenges include the validity of digital agreements, verification of the skills of parties who are only identified through public addresses, potential errors in oracles as an external data source, and potential misuse of technology due to blockchain anonymity, which makes it difficult to prove causa that is halal. In addition, the lack of technical standards regarding code audits, automatic dispute resolution mechanisms, and accountability flows in the event of a bug in smart contracts adds to the legal vulnerability of the parties to the transaction. Thus, this study emphasizes the need to develop special regulations or integrated technical guidelines that can ensure legal certainty, protect parties, and support the safe and sustainable use of smart contracts in Indonesia's digital economy ecosystem.
Abstract We propose federated quantum randomness with client-side sanity (FQR-CSS), a federated architecture that supplies continuously verifiable quantum entropy to cloud hardware security modules (HSMs) and key management services (KMS). In FQR-CSS, each quantum random number generator (QRNG) node emits a randomness contribution along with a post-quantum zero-knowledge proof (ZKP) attesting to device-level operational predicates. An aggregation layer verifies these proofs, runs Byzantine fault tolerance (BFT) consensus (instantiated via HotStuff) over accepted contributions, and publishes a mixed output with an integrity token. We introduce the security notion of verifiable quantum randomness (VQR), comprising unpredictability, quantum-origin guarantee, and federated integrity. We prove VQR under concrete post-quantum cryptographic assumptions. Our proofs utilize Track-A constructions (ZKPs over classical measurement logs), which are fully implementable today. We further outline a theoretical roadmap for Track-B (direct quantum state verification) to guide future research directions. Our empirical evaluation of a post-quantum zk-STARK (Track-A) demonstrates prover latencies of approximately 26 ms for synthetic statistical predicates (K=1024), with sub-millisecond verification times, proof approximately 2.6 KB, and an estimated end-to-end WAN+HotStuff latency approximately 396 ms in our conservative model.
Open access
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
This paper explores the intersection of blockchain technology, cryptocurrencies, and the Metaverse, offering a strategic assessment of their impact on global economic governance within a geopolitical context marked by fragmentation and uncertainty. Grounded in a critical review of the academic literature (2016–2024), the research investigates how decentralized digital infrastructures challenge traditional paradigms of institutional control, monetary sovereignty, and financial regulation. By analyzing key technological mechanisms—distributed ledger technologies (DLTs), smart contracts, non-fungible tokens (NFTs), and decentralized autonomous organizations (DAOs)—the paper proposes an integrated "risks–opportunities" framework, relevant to innovation, regulation, and strategic governance. Identified risks include regulatory asymmetries, technical vulnerabilities, and the concentration of power within systems that claim to be decentralized. At the same time, new opportunities emerge through participatory governance models, cross-border financial inclusion, and the possibility of reimagining global economic coordination beyond traditional intermediaries. The paper argues that the convergence of blockchain-based technologies within immersive environments such as the Metaverse can serve both as a catalyst for systemic transformation and as an experimental space for designing future economic architectures—decentralized, programmable, and globally interconnected. In conclusion, it advocates for the recalibration of management and governance models to respond to emerging digital realities, in a balanced approach that integrates openness, innovation, resilience, and institutional accountability.
Fanidio Muhammad Ariq Sugiarto, Nur Chanifah, Siti Rohmah
The rapid development of blockchain technology has introduced smart contracts as automated digital agreements widely used in the Decentralized Finance (DeFi) ecosystem. These contracts operate without intermediaries and execute transactions based on algorithmic conditions, creating new legal and sharia implications. This study aims to analyze the validity of smart contracts as akad (contracts) within the framework of fiqh muamalah and to formulate regulatory needs based on maqāṣid al-sharī‘ah and positive law. This research uses normative juridical methods with statutory, conceptual, and sharia approaches by examining legal doctrines, regulations, and Islamic jurisprudence principles. The results show that smart contracts can qualify as valid akad if pillars and conditions of contract are fulfilled, including parties, consent, object, and lawful purpose, although digital consent and automated execution require interpretative expansion. From the maqāṣid perspective, smart contracts potentially support protection of wealth (ḥifẓ al-māl), transparency, and efficiency, but also pose gharar and risk if coding errors and regulatory gaps exist. Therefore, integrative regulation and sharia compliance standards are necessary to ensure legal certainty and maslahah in DeFi transactions.
SARMF (Smart Contract Automated Remediation and Mitigation Framework) is a structured and reproducible security engineering pipeline designed for vulnerability detection, taxonomy alignment, automated remediation, and adversarial validation of Ethereum-compatible smart contracts. This operational protocol presents a structured workflow for smart contract vulnerability detection and automated mitigation within blockchain-based systems. The methodology integrates deterministic environment setup, multi-tool static analysis, vulnerability normalization using standardized taxonomies, rule-based automated patch generation, and dynamic adversarial validation. By combining static detection tools with controlled refactoring patterns and behavioral verification, the framework ensures reproducibility, traceability, and measurable performance impact assessment. The protocol concludes with comprehensive audit reporting and archival procedures to support transparency and independent verification. This workflow provides a systematic foundation for secure smart contract lifecycle management in decentralized applications. Unlike traditional audit checklists, this framework operationalizes vulnerability detection, taxonomy alignment, automated remediation generation, and validation feedback loops into a unified reproducible security engineering pipeline. Key Contributions of SARMF: Deterministic environment and compilation reproducibility model. Unified multi-tool vulnerability normalization aligned with SWC taxonomy. Rule-based automated mitigation generation preserving semantic integrity. Iterative validation loop combining static, adversarial, and fuzz testing. Structured audit archival enabling independent verification and traceability.
Sustainable finance models are most often built for contexts characterized by institutional stability, effective governance, and functioning capital markets. In fragile states, such conditions are often absent. This paper revisits sustainable finance through the case of Lebanon, where the post-2019 financial collapse rendered conventional instruments, such as ESG frameworks, green bonds, and sustainability-linked loans, difficult to implement and contextually irrelevant. Drawing on literature regarding sustainable finance, degrowth and post-growth economics, and the political economy of fragility, the paper proposes a conceptual framework for Agile Sustainable Finance: a model that explains how financial practices oriented towards sustainability can persist despite institutional collapse with agility operating as the mediating capability. The model positions agility as the central capability enabling households, firms, and communities to reorganize financial life amid institutional erosion, liquidity shortages, and involuntary degrowth. It highlights how informal credit systems, remittances, community financing, and decentralized energy solutions become essential tools for resilience and ecological sufficiency in collapsed economies. By reframing finance as a mechanism for survival, redistribution, and basic sustainability rather than growth, this conceptual study offers a theoretical model that bridges domains that rarely intersect: sustainable finance and fragile-state dynamics.