Blockchain Papers

Juan Kong, Chen Lou

No abstract is available for this record.

Alberto Rocha

No abstract is available for this record.

Baixuan Tian

This dissertation presents the design, implementation and evaluation of a blockchain-based e-voting prototype which supports ballot confidentiality, verifiability and auditability by combining homomorphic encryption, zero-knowledge proofs and Merkle inclusion proofs. The key innovation of this work lies in a unified on-chain protocol that cryptographically binds each ballot's Poseidon commitment to its ElGamal ciphertext through a shared binding hash, enabling Groth16-verified tallying and anonymous receipt-based audit without any trusted middleware. In the voting phase, the voter encrypts a one-hot ballot, computes a commitment and a ciphertext-binding hash, and submits them together with a zero-knowledge proof that the ballot is well formed without revealing the selected candidate. During the tallying stage, all encrypted ballots are off-chain aggregated, the final results are accompanied by a proof of correct decryption. Voters can confirm their ballots are included by using locally stored receipts and an audit bundle provided by the administrator. The prototype is implemented with Solidity for the smart contracts, Circom and snarkjs for zero-knowledge circuits, JavaScript for frontend interaction and Python for local serving and cryptographic validation. It was tested locally with Hardhat and then deployed to the Sepolia testnet for more realistic on-chain execution and gas measurement. The results show that the system functions as a research prototype, although further hardening is still needed before any production use.

Liang Xue, Xiaodong Lin, Xuemin Sherman Shen

No abstract is available for this record.

Yang Liu, Tantan Yang, Feng Wang, Fangchao Tian

No abstract is available for this record.

Kiat Sen Jaron Lim

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.

Goshgar Ismayilov

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.

Feng Yang, Tong Yang, X X Xu, Dongyang Chen · 5 authors

No abstract is available for this record.

Carvalho Tiago, Rijo Gon鏰lo, Gon鏰lves Lio, Amorim Vasco · 5 authors

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.

Piyoosh Rai

No abstract is available for this record.

Idowu Adetona Ayoade, Omowunmi Mary Longe

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.

Maria Smith

[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.

Gboraloo A. W., Eke B., Onuodu F. E.

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.

Haodi Wang, Danyang Zhang, Tangyu Jiang, D. Wang · 7 authors

No abstract is available for this record.

Min Zhang, Yuehua Chen, Xiyuan Fu

No abstract is available for this record.

Steven Paul Nohr

<b><i>Zero-knowledge proof (ZKP) systems</i></b> such as<b><i> zk-SNARKs</i></b> and<b><i> zk-STARKs</i></b> are increasingly promoted as comprehensive solutions for privacy, scalability, and selective disclosure in blockchain-based systems. While these cryptographic primitives provide strong guarantees regarding computational correctness and data confidentiality, they are frequently mischaracterized as substitutes for regulatory compliance, legal enforceability, or supervisory control mechanisms. This paper presents a structural and functional comparison between zero-knowledge proof systems and the Crystal Validator™ (CV), a logic-layer enforcement architecture designed to encode and enforce jurisdiction?aware regulatory requirements. We demonstrate that cryptographic validity proofs are orthogonal to—and insufficient for—legal compliance, accountability, and regulatory supervision as required under frameworks such as the EU Markets in Crypto-Assets Regulation (MiCA). We argue that regulatory enforcement logic must exist above cryptographic proof layers and cannot be replaced by them without introducing systemic compliance risk.

Hong Min, Yousef Ibrahim Daradkeh, Jung Taek Seo, Mohd Anjum · 5 authors

This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things (IoT)-enabled smart contract systems. The integration of IoT, edge computing, and blockchain introduces significant challenges, including limited device capacity, high verification cost, and scalability constraints. Existing blockchain verification approaches depend on computationally intensive cryptographic operations that are inefficient for resource-constrained IoT devices, resulting in increased latency, energy consumption, and transaction costs. To address these issues, this study proposes the Zero-Knowledge Fuzzy Logic Offloading and Rollup (Z-FLOR) framework, an adaptive and energy-efficient model designed to enable secure and verifiable computation in IoT-based smart contract systems. The proposed framework integrates three key components. First, a zero-knowledge proof-based verification model using the Groth16 zkSNARK module generates compact and privacy-preserving proofs that enable fast and reliable verification. Second, a Fuzzy Logic–Driven Energy-Aware Offloading module dynamically allocates computational tasks between IoT devices, edge servers, and cloud platforms based on energy availability, network delay, and device reliability. Third, an Optimistic Rollup Verification module aggregates proofs off-chain and submits them in batches to reduce gas costs and enhance scalability. Extensive simulation and experimental evaluation across diverse IoT scenarios demonstrate the effectiveness of the proposed computational framework. Results indicate that Z-FLOR achieves 99.7% verification accuracy and 98.9% proof compression efficiency, while gas cost analysis indicates gas cost reductions in the range of 80%–98%. Z-FLOR additionally achieves a 44.0% reduction in latency, 51.0% savings in gas costs, and 38.0% energy consumption compared to baseline approaches. These findings highlight the capability of the proposed approach to serve as a scalable and energy-efficient modeling solution for secure IoT smart contract execution in decentralized environments.

Nai-Hui Chia, Kai-Min Chung, Xiao Liang, Jiahui Liu

No abstract is available for this record.

Tan Nguyen Ngo

Machine learning-based intrusion detection systems can identify malicious network activity, but their predictions and explanations are typically accepted without verifying that they were derived from the same input. This thesis develops a public-model/private-input zero-knowledge framework for certifying a prediction and its semantic explanation while keeping the processed network-flow features private. The framework is instantiated through a TON_IoT intrusion detection case study in which 104 processed features are mapped into five semantic groups. Logistic Regression is used as the proof-compatible public model, while XGBoost provides a stronger plaintext performance baseline. The main technical contribution is an implementation-backed proof relation that jointly verifies Logistic Regression inference and an ordered top-3 semantic explanation from the same private input. Under a fixed training-mean reference, semantic-group Exact SHAP for the linear score reduces to a direct group-wise weighted sum, enabling its implementation in a Circom circuit and verification using Groth16. The quantized relation achieves more than 99.99% prediction agreement with the floating-point model, while ordered top-3 explanation agreement is approximately 93.8%. Valid proofs are accepted, whereas incorrect predictions, malformed rankings, and out-of-range inputs are rejected. The results demonstrate the feasibility of cryptographically binding a prediction and a semantic explanation under private tabular inputs. The implemented relation remains limited to a public linear model, fixed semantic groups, and an approved reference vector, and does not provide arbitrary-model explanation verification, model confidentiality, or production-ready provenance.

Lukas Aumayr, Zeta Avarikioti, Matteo Maffei, Giulia Scaffino · 5 authors

No abstract is available for this record.

Hanyu Mao, Tiezheng Nie, Minghe Yu, Xiaomei Dong · 6 authors

No abstract is available for this record.

Sayan Bairagi

No abstract is available for this record.

Mohammad Muavia

No abstract is available for this record.