This project explores the Groth16 zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) protocol, with an emphasis on accessibility and practical understanding. It begins with a review of zero-knowledge proofs, non-interactive zero-knowledge proofs, and zk-SNARKs, followed by a structured explanation of the Groth16 construction, from Rank-1 Constraint System (R1CS) and Quadratic Arithmetic Program (QAP) representations, to the full formulation incorporating trapdoor elements and zero-knowledge randomness that is supported with a working Python implementation over the BN254 elliptic curve. These theoretical concepts are then applied in SudoZKu, a browser-based Sudoku game that demonstrates a complete end-to-end zk-SNARK real-world implementation pipeline. This system uses Circom for circuit design and snarkjs for Groth16 proof generation and verification, illustrating how high-level computations can be translated into succinct, verifiable proofs within a practical setting. Experimental evaluation then compares Groth16 and another zk-SNARK known as Permutations over Lagrange-bases for Oecumenical Non-interactive arguments of Knowledge (PLONK). Results show that Groth16 achieves approximately 1.9x smaller proofs and up to 16x faster proof generation than PLONK, while both are able to complete verification under 65 milliseconds. The project then concludes by analysing the key trade-offs for Groth16, including trusted setup requirements and a lack of post-quantum security, and outlines future research directions such as on-chain verification and privacy-preserving uses of Groth16.
Blockchain technology has revolutionized numerous industries by providing decentralized, transparent, and immutable ledgers. However, its adoption is hindered by persistent security challenges, including arbitrage attacks, liquidity exploits, and noncompliance with antimoney laundering (AML) regulations. This paper proposes an enhanced framework to address these issues, combining dynamic pricing mechanisms, AI‐based anomaly detection, and regulatory compliance checks within a multilayered architecture. The framework is composed of five interconnected layers: the input layer for data collection and validation, the data warehouse layer for structured data classification, the processing layer for anomaly detection and pricing adjustments, and the decision layer for transaction validation, execution, and reporting. The integration of these layers ensures robust security and compliance mechanisms, reducing system vulnerabilities while optimizing efficiency. To validate the proposed framework, we conducted simulations using real‐world blockchain scenarios, including decentralized finance (DeFi) platforms and cryptocurrency exchanges. Results demonstrate significant reductions in arbitrage opportunities and liquidity risks, with improved accuracy in anomaly detection and compliance adherence. For instance, the dynamic pricing mechanism mitigated 87% of arbitrage attack attempts, while the AI‐based anomaly detection achieved an 89% accuracy rate in identifying high‐risk transactions. This study provides actionable insights and a scalable solution for enhancing blockchain security and trust. Future work will focus on integrating cross‐chain interoperability, real‐time threat intelligence, and privacy‐preserving techniques to further expand the framework’s applicability. By addressing critical vulnerabilities, this research contributes to the development of secure, transparent, and compliant blockchain ecosystems, paving the way for wider adoption across industries. Unlike previous blockchain security models, our framework introduces a real‐time, AI‐enhanced risk assessment mechanism that dynamically updates transaction risk scores, mitigating financial threats in decentralized environments. This holistic approach provides a scalable, explainable, and adaptive security system that not only protects decentralized financial infrastructures but also aligns with emerging regulatory requirements, ensuring long‐term applicability.
Climate adaptation policies in the Global South frequently falter due to the concentration of authority and finance at central institutional levels, which marginalizes local resilience planning. While global frameworks advocate for participation, current systems continue to privilege top-down priorities over community realities. This study proposes Climate Swaraj, a decentralized governance framework that re-conceptualizes adaptation as a political-institutional process rather than a technical intervention. Utilizing a Qualitative Comparative Analysis (QCA) across India, Kenya, and Bangladesh—validated by a case study of the Meenangadi Panchayat in Kerala—this research demonstrates that devolved governance significantly enhances resilience. Integrating resilience theory with climate justice and alternative development perspectives, the analysis argues that adaptation outcomes are a function of institutional design. The Climate Swaraj model is operationalized through five interlinked pillars: governance sovereignty, resource decentralization, localized finance, participatory planning, and ethical low-carbon development. By shifting the focus to community autonomy and ecological responsibility, this framework provides a scalable pathway for equitable and durable climate resilience in the Global South.
Social and Economic Development in India
Sustainability and Climate Change Governance
Conservation, Biodiversity, and Resource Management
Oracles have become a foundational component of decentralized finance and cross-chain systems by enabling smart contracts to consume external data such as price feeds, timestamps, and event confirmations. However, oracles are fundamentally limited to validating facts and cannot determine whether a transaction or state transition is legally, contractually, or behaviorally permitted. As regulatory regimes such as the European Union’s Markets in Crypto-Assets Regulation (MiCA) impose enforceable obligations on token issuers, service providers, and infrastructure operators, this limitation creates a critical enforcement gap. This paper introduces a Logic Validation Layer (LVL), implemented via the Crystal Validator™, which enforces jurisdictional, behavioral, and policy-based constraints directly within token execution paths. By separating factual data delivery, cross-chain transport, and logic enforcement into distinct architectural layers, the proposed model enables deterministic compliance without sacrificing interoperability, composability, or performance. The paper argues that while oracles remain necessary, logic-level validation has become unavoidable for regulated cross-chain finance.
Este ensayo jurídico aborda el análisis de la aplicación de los contratos inteligentes (Smart Contracts) al contrato de seguro en el marco jurídico colombiano. Parte de una explicación sobre qué son los contratos inteligentes y cómo funcionan mediante la tecnología Blockchain, destacando sus ventajas en términos de seguridad, automatización y transparencia. Posteriormente, se centra en el contrato de seguro como una tipología contractual particularmente apta para esta implementación, debido a sus características propias. Este trabajo busca determinar si estos contratos digitales son válidos y útiles en el ámbito asegurador, analizando sus efectos jurídicos desde una perspectiva hermenéutica y a la luz de la teoría de la justicia de John Rawls, con el fin de evaluar si esta innovación tecnológica puede mejorar la relación entre asegurador y asegurado, garantizando una ejecución más eficiente y justa del contrato.
This essay examines the challenges that smart contracts pose for contractual interpretation. While smart contracts are often viewed as unambiguous because they operate through deterministic computer code, disputes may nevertheless arise where the outcome produced by the code diverges from the parties’ understanding of their agreement. The essay argues that these disputes are not adequately addressed by treating code as a definitive expression of contractual intention or by adopting the ‘reasonable coder’ standard proposed by the United Kingdom (‘UK’) Law Commission. The reasonable coder standard is theoretically incoherent because it lacks alignment, generality, and reviewability and therefore cannot perform the descriptive and normative functions served by the reasonable person standard. Thus, this standard should be discarded entirely. Interpretation should continue to be governed by the reasonable person standard, while relaxing the evidentiary constraints imposed by the parol evidence rule. By permitting greater reliance on contextual materials, including pre-contractual negotiations and subsequent conduct, courts can more accurately identify the bargain the parties intended to conclude without having to invoke a conceptually unstable standard.
Smart contracts are high-stakes software: their immutable, publicly accessible, code may govern assets worth millions, meaning that even minor defects can have severe consequences. The most used techniques to ensure smart contract correctness are testing and formal verification. Testing is almost always employed but is often restricted to unit tests (which often miss edge cases) and has limited coverage, while formal verification can provide strong guarantees but is often costly and complex to apply, demanding substantial time and expertise. Property-based testing bridges this gap by exploring large input spaces and shrinking failures to minimal counterexamples, helping uncover defects early in development. Formal verification can be left to critical features once testing has filtered out common issues. To add to the challenges smart contract developers face, most languages used were not designed with safety and security guarantees built-in. Daml is a smart contract language designed with correctness in mind, featuring a strong static type system, functional programming paradigms, and built-in abstractions for common smart contract patterns. However, Daml currently lacks support for property-based testing, limiting developers' ability to systematically explore input spaces and verify contract properties. This paper introduces Hypothesis2Daml, an open-source library that brings property-based testing to the Daml ecosystem by connecting the Hypothesis testing framework with the Daml JSON API. Hypothesis2Daml enables developers to specify invariants, preconditions, and stateful workflows over realistic ledger interactions, while providing automatic input generation, shrinking, and isolation of ledger state between test cases. The approach is evaluated using a benchmark consisting of eight contracts, three Daml templates, and twenty-eight property-based tests covering happy paths, negative cases, and alternative interaction orders. The results show that property-based testing is feasible for Daml smart contracts, can systematically expose violated properties with minimal counterexamples, and supports effective debugging of realistic, stateful workflows.
Zero-knowledge proof is a special cryptographic technique that allows a prover to convince a verifier about the correctness of a claim without explicitly disclosing the claim itself. With the advancements of blockchain technologies, zero-knowledge proof has been successfully integrated into many decentralized applications over the years. ZoKrates, with its ease-of-use and direct integration to blockchain platforms, has emerged as a leading framework for developing, generating and verifying zero-knowledge proofs. This survey compiles a corpus of 347 documents that cite the original research work of ZoKrates by considering the period ranging from 2018 to 2025. Out of this corpus, this survey systematically selects and analyzes a total of 87 different documents including only peer-reviewed publications and excluding the gray literature. To the best of our knowledge, this is the first survey in the literature to follow a systematic approach to analyze the privacy- preserving applications in blockchain from the perspective of ZoKrates. This survey presents three different classifications over the documents with respect to (i) the applications they develop, (ii) the challenges they frequently encounter and (iii) the metrics they often use to measure performance of their techniques. Based on the challenges identified, this survey finally discusses numerous future research directions to promote potential advancements in the field and attract the attention of scientific and industrial communities. Feedback from readers regarding any inaccuracies or misinformation in this survey is welcome.
Traditional electronic Kanban (eKanban) systems depend on manual scans and offer only discrete material visibility, limiting responsiveness and automation in lean manufacturing environments. These operational bottlenecks are magnified in high-mix contexts, where delayed replenishment signals degrade flow stability, increase work-in-progress, and hinder sustainable material handling. Furthermore, vendor-specific systems lack interoperability for scalable automation, constraining the development of intelligent manufacturing solutions. This work investigates whether zone-based replenishment automation can be enabled through real-time locating systems (RTLS) using open interoperability standards, addressing a gap in empirical validation of such approaches. A middleware architecture was developed that integrates ultra-wideband (UWB) positioning, an Omlox-compliant location middleware (DeepHub), and a cloud-based eKanban system to replace manual triggers with geofence-driven order creation. The novelty of this study lies in demonstrating a fully automated Kanban signaling loop built on the open Omlox standard, providing vendor-independent RTLS interoperability and eliminating human intervention in replenishment signaling. This contributes new knowledge on how continuous location data can be converted into actionable replenishment events in a standards-based, modular manner, enabling more intelligent and autonomous material-flow control. A controlled proof-of-concept experiment simulating shop-floor conditions showed that the system achieved a 100% detection success rate, zero duplicate orders, and an average trigger-to-action latency of 2.7 s, while automatically recovering from authentication and WebSocket failures. These results provide the first empirical evidence that Omlox-compliant RTLS middleware can reliably support zone-based eKanban automation. The findings have direct implications for intelligent and sustainable manufacturing by demonstrating a scalable pathway toward interoperable, real-time material-flow systems that reduce manual intervention, avoid unnecessary handling, and lower work-in-progress. More broadly, the work addresses the current lack of empirical validation of open-standard RTLS integration within lean and sustainable production environments.
Electromobility requires transactive coordination that respects distribution-network limits while preserving auditability and privacy. This study presents a reproducible peer-to-peer energy trading system that integrates a network-constrained market with permissioned blockchain settlement. The market solves a convex welfare program with linearized power-flow limits and recovers nodal prices from dual variables to match bids and offers and determine clear quantities. Settlement uses Hyperledger Fabric via the Gateway API, including proposal endorsement, ordering, validation, and commit notifications. Meter evidence is hashed and, when necessary, stored with private data collections. A co-simulation harness links MATLAB/Simulink and MATPOWER for feeder dynamics and price formation with chaincode and client logic for settlement. Three case studies are evaluated: an urban microgrid, a suburban microgrid, and a mobile electric-vehicle swarm. An Ethereum testnet serves as a public-chain baseline. In the testbed, a tuned Fabric configuration sustained approximately 1.6 to 1.7 thousand transactions per second with 99th-percentile submit-to-commit latency near one second and full deadline compliance at a one-second clearing cadence. Energy delivery accuracy remained tight, Multi-Version Concurrency Control conflicts were low, and dynamic nodal prices reduced EV charging cost relative to a flat tariff while signaling congestion through predictable rent patterns. The contribution is a deployable blueprint that connects network economics to verifiable settlement, with an open repository, benchmarking artefacts, and practical targets for endorsement width, block size, and timeouts, and clear pathways to field trials, stochastic and robust clearing, zero-knowledge meter proofs, and city-scale deployment.
[Depreciated and replaced by V3] This pre-V3 paper is replaced by the corresponding V3 clean-room reconstruction: There Is No Nothing: A Premise-Free Operational Foundation and an Open Verification Platform for Smithian Fold Theory. The V3 source platform is https://github.com/MettaMazza/ernos-labs-sft-platform. The original DOI, concept DOI, version number and files are preserved for transparent historical provenance; this record must not be presented or cited as current V3 work. Opaque predictive reliability is valuable evidence of performance; it is not by itself a derivation, causal explanation or proof. This paper establishes the Smithian Fold Theory standard: one machine-checked self-proven theorem, zero axioms, zero fitted parameters, exact trace to the One, independent certificates, public evidence and a halt when forcing breaks. The synchronized corpus executes 326 suites and 2,002 exact checks with zero failures, with all 326 generated-C certificates identical to source. Its computational proofs carry the same method into sealed blind protein structure, exact and competitive Chess, exact and competitive Go, native zero-trained-parameter UnisonAI and measurement of fold law inside trained weights. The paper protects authorship and empirical method: agents do not declare Maria Smith's findings, convert their auxiliary failures into her results or impose incumbent theoretical walls. Benchmark victories remain explicit objectives; development evidence directs construction; every positive result is investigated and retained. Scientific author and publication authority: Maria Smith, Ernos Labs. Open source: Smithian Fold Theory of Everything.
Seema C K, Sharan V Talwar, subhash Chandra K R Patel, S. E. R. Sacha Emile R.
The Internet of Things (IoT) presents critical security challenges including device identity spoofing, replay attacks, and data tampering across billions of deployed endpoints. This work presents ZK-IoTChain, a blockchain-enabled security framework that integrates zk-SNARK-based device authentication with Merkle-anchored data integrity in a unified architecture. The proposed system employs a three-layer design consisting of device-side proof generation, on-chain Groth16 verification, and IPFS-based off-chain storage. This architecture ensures privacy-preserving authentication while maintaining scalability and cost efficiency. Experimental evaluation on the Ethereum Sepolia testnet demonstrates a mean proof generation latency of 3.21 seconds and on-chain verification latency of approximately 125 milliseconds, with an average gas cost of 278,400 per authentication. The framework achieves a 99.98% reduction in storage cost compared to full on-chain approaches. Security analysis under the Dolev–Yao adversary model confirms effective mitigation of identity spoofing, replay attacks, data tampering, and man-in-the-middle (MITM) attacks. The results highlight ZK-IoTChain as a practical and efficient solution for secure, scalable, and privacy-preserving IoT ecosystems.
Decentralized exchanges (DEXs) have emerged as a foundational component of blockchain-based financial systems, enabling trustless asset trading without centralized intermediaries. However, the transparency of public mempools introduces significant vulnerabilities, including front-running, sandwich attacks, transaction reordering, and broader information asymmetry. In response, Cryptographic mechanisms such as Zero Knowledge (ZK) based architectures and commit reveal schemes are increasingly proposed as a solution to these vulnerabilities. This research systematically reviews the structural transparency paradox and cryptographic architectures in Decentralized Exchange based Automated Market Makers (DEX-AMM), evaluate their effectiveness in mitigating Maximal Extractable Values (MEVs), analyze computational complexity trade-offs including proof generation/verification costs, gas overhead, latency, and throughput, and identify why commit-reveal may offer superior practical viability despite zk-proofs' stronger theoretical privacy guarantees. A comprehensive search was conducted across arXiv, IEEE Xplore, ACM Digital Library, Scopus, Web of Science, Google Scholar including grey literatures for studies published between 2021 to 2026. Findings indicate that ZK-based approaches provide strong cryptographic privacy guarantees but often incur computational overhead and integration complexity, zk-rollups provide strong validity guarantees through cryptographic proofs, but their practical security depends heavily on the sequencer layer used by ( zkSync, StarkEx, and Loopring) which is responsible for transaction ordering, which can censor, delay, reorder transactions or cause failure of execution, while Commit-reveal schemes may be superior for real-world DEXs due to their constant time hash-based complexity (O(1)), lower gas costs, sub-second latency, and simpler implementation, despite requiring two-transaction UX friction, which can be mitigated through wallet automation. The computational efficiency advantage of commit-reveal becomes critical as DEX transaction complexity increases, where zk-circuit depth grows exponentially. Future research should prioritize optimizing zk-circuit efficiency, developing zk-commit-reveal hybrids system that balance cryptographic strength with computational practicality, and advancing hash-based commit-reveal schemes with UX improvements. DEX developers should prioritize commit-reveal for latency-sensitive applications and zk-proofs only when strongest cryptographic privacy is mandatory.