Jun 11, 2026·Universitatea Titu Maiorescu, the 19th international conference, education and creativity for a knowledge based society, informatics proceeding book
This paper presents a comparative study between two leading blockchain platforms—Hyperledger Fabric and Ethereum—with emphasis on their architectural design, performance characteristics, and security mechanisms in the context of enterprise applications. The study aims to identify key differences between permissioned and public blockchain models, focusing on scalability, consensus efficiency, and data confidentiality. A controlled experimental environment was developed using Docker-based deployments for both platforms, and performance was evaluated through Hyperledger Caliper using standardized workloads. Metrics such as transactions per second (TPS), latency, resource consumption, and failure rates were analyzed under varying network sizes. The results indicate that Hyperledger Fabric achieves significantly higher throughput (≈900 TPS) and lower latency (<200 ms) compared to Ethereum (≈25 TPS, ≈1 s latency), due to its deterministic Raft consensus and modular architecture. Ethereum, however, demonstrates superior decentralization and transparency suitable for public and decentralized applications. The findings highlight that both platforms are complementary: Hyperledger Fabric is optimized for controlled, high-performance enterprise use, while Ethereum excels in open, trustless environments.
Data privacy concerns have become more critical than ever as machine learning and applied intelligence systems permeate sensitive industries such as healthcare, finance, national security, and personal services. This necessitates the development of privacy-preserving strategies for protecting private information while retaining the utility of intelligent models. This survey provides a comprehensive overview of privacy-preserving machine learning, with an emphasis on the cryptographic and statistical methods that are transforming how safe learning systems are built. The study starts by examining the most important components of the machine learning model and figuring out which of these may be protected to solve important privacy problems. The article then explores modern cryptographic techniques, including homomorphic encryption, zero-knowledge proofs, secure multiparty computations, and a statistical approach called differential privacy, that support contemporary privacy-preserving machine learning solutions. The study then explores how these strategies are applied independently and in hybrid systems to achieve accuracy, efficiency, and balance of privacy. This survey provides promising direction for protecting sensitive information during real-world model training and inference, offering insights into the design of trustworthy applied intelligence systems.
Blockchain oracles bridge on-chain smart contracts and off-chain data sources, but encrypted off-chain data still raises two practical challenges: how to verify retrieval integrity without exposing sensitive values, and how to keep verification information fresh when the off-chain data set changes. Existing oracle and outsourced-database retrieval mechanisms often rely on plaintext verification, heavy cryptographic proofs, or static authentication structures, which limits their applicability to latency-sensitive IoT and decentralized finance scenarios. To address these issues, this paper proposes a retrieval integrity verification mechanism based on CKKS approximate homomorphic encryption and an authenticated index named CKKS-Auth Tree. The proposed mechanism verifies encrypted query results through homomorphically aggregated metadata, while smart contracts record versioned verification commitments to detect stale or replayed results after updates. The scope of the mechanism is the integrity, completeness, privacy, and freshness of data after commitment and upload; verifying the physical authenticity of the original data source is outside the core threat model. Experimental results show that the proposed scheme reduces authentication and verification overhead compared with existing retrieval verification methods while supporting encrypted metadata updates and on-chain synchronization.
Archy Renaldy Pratama Nugraha, Yandra Arkeman, Irman Hermadi, Yani Nurhadryani
Digital identity verification in e-governance faces a trilemma between security, scalability, and regulatory compliance with Indonesia's Personal Data Protection Law (UU PDP). To resolve this, in this paper, we propose the ZMC-Framework, a blockchain-based hybrid architecture integrating Zero-Knowledge Proofs (ZKPs) for privacy-preserving verification and Merkle Trees for efficient, scalable data integrity on-chain. Its core innovation is a Legal Proof Protocol with 3+1 parameter augmentation, which cryptographically binds static identifiers to a user-controlled secret, ensuring compliance with UU PDP (data minimization) and UU ITE (authentication integrity) while aligning with key controls of the international ISO/IEC 27001:2022 standard. Evaluated on Polygon Mainnet, the framework demonstrates 29.9% lower operational costs for batch verifications and 50% better storage efficiency compared to pure ZKP systems. These results validate a practical solution to the verification trilemma, providing a secure, scalable, and legally sound foundation for public service identity management in Indonesia's digital governance ecosystem.
C. Rupa, K. Vijaya Bhaskar Reddy, Srinivas Jagirdar, Srinivas Rao Pulluri · 6 authors
Land registration and record management systems worldwide continue to face significant challenges, including document fraud, long processing times, and inefficient maintenance procedures. Traditional methods involve several technical limitations that reduce reliability and transparency. To address these issues, the proposed system leverages blockchain technology to improve process efficiency, data integrity, and security in land registration workflows. In the proposed framework, users upload property details and supporting land documents while initiating a sale. However, fraudulent document uploads remain a common issue, enabling sellers to receive payments using forged records without the buyer’s knowledge. To mitigate such risks, a Convolutional Neural Network (CNN) is integrated to authenticate and validate uploaded land documents before further processing. Only documents verified as authentic are stored on the blockchain. The government authority converts these validated documents into Non-Fungible Tokens (NFTs) and mints them on the Ethereum blockchain. A unique hash is generated for each document, enabling secure verification and traceability through platforms such as Etherscan. Once the documents are confirmed to be valid, the property is approved for sale, and the ownership transfer between the seller and buyer is securely executed through the blockchain enabled system. We evaluated the performance of proposed framework by considering both blockchain performance metrics and CNN evaluation metrics.
Nazneen Fatema, Abdullah Mohammed Ibrahim, Jesmin Sabnam, Abdullah Mohammad Ismail
This bibliometric study maps research trends in cybersecurity and data breaches within the financial sector from 2020 to 2024, analyzing 7355 documents from the Web of Science. The findings reveal a rapidly expanding and interdisciplinary field, driven by the digital transformation of finance, heightened cyber threats, and the impact of global events such as the COVID-19 pandemic. The research landscape has evolved from descriptive, technical studies to sophisticated analyses incorporating network theory, econometrics, and risk management. Most prolific authors and sources, such as IEEE, demonstrate strong international collaboration and significant citation impact, with China, the USA, and the UK leading in citations. Co-citation network analysis identifies three major intellectual clusters: economic modeling of cyber risk, network-based risk propagation, and systemic macro-financial implications of cyberattacks. The study highlights an increasing focus on quantifying the financial and reputational impacts of cyber incidents, making research directly relevant to business and regulatory stakeholders. Limitations include reliance on a single database and quantitative methods. Future research directions emphasize the security implications of emerging technologies (e.g., quantum computing, decentralized finance, artificial intelligence), behavioral and cultural aspects of cybersecurity, and systemic regulatory challenges. The field is dynamic, reflecting the financial sector’s evolving risk landscape.
Federated Learning (FL) is increasingly deployed in healthcare to enable collaborative intelligence while keeping sensitive data privately at local institutions. However, existing healthcare-oriented FL frameworks still suffer from several limitations: they are vulnerable to adversarial model updates, provide limited transparency and verifiable auditability, and often lack predictable performance under constrained resources. We present IIN-Health, a blockchain-enhanced intelligent fusion network tailored for dependable healthcare FL. IIN-Health adopts a dual-chain architecture with policy-aware access control and auditable provenance tracking to integrate learning, security, and governance in a unified framework. Evidence-Carrying Access Tokens (ECATs), combined with zero-knowledge proofs, are introduced to enforce patient-defined policies and validate access decisions without disclosing sensitive information. In addition, we design MedBFT-Δ, a domain-specific Byzantine fault-tolerant protocol that ensures reliable system behavior in the presence of faulty or malicious participants. We conduct several experiments to validate its feasibility and accuracy on the MNIST dataset. The results demonstrate that IIN-Health achieves smooth and stable convergence, exhibits strong resilience against poisoning attacks, and maintains graceful performance degradation under resource constraints, while preserving verifiable auditability of model updates and data flows. These observations indicate that IIN-Health can provide a practical balance among performance, security, and regulatory compliance, and thus offers a promising foundation for trustworthy and scalable FL deployments in healthcare.
Shiho Kim, Ho Suk, Roberto Di Pietro, Davor Svetinović · 7 authors
ZABAPAD (Zero-knowledge proof And Blockchain for WEB 4.0: Advancing the Post-quantum And Decentralized Era) is a workshop focusing on zero-knowledge technologies, blockchain infrastructure, and post-quantum readiness for the emerging Web 4.0 ecosystem. This workshop emphasizes real-world deployments, empirical measurements, and interoperability across Web and non-Web domains. In particular, ZABAPAD explores the convergence of AIoT and ZKP—redefining identity and trust models beyond SIM in mobile networks, IP in Web 2.0, and NFT in Web 3.0. As AIoT systems evolve toward decentralized, post-quantum infrastructures, ZKPbased authentication and AIoT SIM functionalities are emerging as key enablers of secure, privacy-preserving, and verifiable connectivity among intelligent devices, vehicles, and edge services. This theme extends to ZKML, Layer-2 proving/verification, TEE+ZK integration for verifiable compute, and post-quantum migration of identities, wallets, ledgers, and protocols. Expected outcomes include: (1) a practitioner-oriented adoption playbook, (2) an interoperability and standards checklist, (3) a curated set of reproducible benchmarks and datasets, and (4) a catalog of failure modes and mitigations for domains such as finance, mobility, healthcare, AIoT, public services, supply chain, and AI/ML. ZABAPAD complements the Web Conference and Web 4.0 communities by uniting global researchers and developers to chart actionable, trustworthy pathways toward the post-quantum, decentralized, and intelligent Internet.
Supply Chain Finance (SCF) enhances financial liquidity, optimizes operational efficiency, and strengthens collaboration among supply chain stakeholders. Traditional SCF systems are vulnerable to economic risks, fraud, and a lack of transparency due to centralized data control and limited real-time monitoring capabilities. Current systems rely on slow central financial bodies and static credit evaluations, lacking dynamic risk assessment and mechanisms to build stakeholder trust. This research proposes Bi-MATRA, a Blockchain-Integrated Multi-Agent Trust and Risk Assessment system. The goal is to create a decentralized, intelligent risk evaluation system for real-time trust computation and automated decision-making in SCF contexts. Bi-MATRA combines blockchain's immutability and transparency with a multi-agent system that automatically monitors and assesses risk. Bi-MATRA uses an adapted Eigen Trust algorithm on the blockchain to calculate trust. Agents rate each other based on interaction history, and transitive trust linkages determine global trust ratings. Smart contracts for dynamic trust-based decision-making update these scores. Smart contracts enforce predefined financial agreements, automating processes and reducing the need for intermediaries. Agent-based modelling was employed to develop a blockchain-based simulation testbed for assessing Bi-MATRA's responsiveness, accuracy, and resilience. The framework was more efficient at trust validation and risk identification than typical SCF systems. Key studies reveal that Bi-MATRA saves transaction clearance time by 35% and improves early risk detection by 28%. EigenTrust-based trust computation builds agent trust and accountability, enhancing financial interactions. In conclusion, the Bi-MATRA framework offers a scalable, decentralized approach to intelligent risk assessment and trust-building in blockchain-enabled supply chain finance systems.
Inseong Jeon, Sundeuk Kim, Hyunwoo Kim, Hoh Peter In
<title>Abstract</title> As Solidity becomes the dominant language for blockchain smart contracts, efficient debugging grows increasingly critical. However, current Solidity debugging remains inefficient: developers must compile, deploy, set up transactions, and step through execution line-by-line to examine each variable. This process is too slow for practical use. To address this challenge, this paper presented SolQDebug, the first interactive source-level debugger for Solidity that delivered millisecond feedback directly on source code. Developers specify input value ranges through annotations and compare them against abstract interpretation results, thereby enabling exploration of contract behavior across multiple execution paths. SolQDebug was evaluated on 30 real-world functions from DAppSCAN, achieving 350$\times$ faster debugging (0.15s vs. 53s per function) than Remix IDE. The evaluation provided debugging insights: overlapping annotation patterns improved precision in most Solidity debugging scenarios, while analysis of diverse loop patterns demonstrated improved convergence while preserving soundness guarantees. These results demonstrated that SolQDebug enabled interactive debugging for Solidity development.
Abstract This concept documents a complete physics-first authorisation architecture for the quantum-permanent era — applicable across AI cluster security, interbank settlement, space and interplanetary infrastructure, critical infrastructure protection, digital identity, supply chain integrity, and optional democratic participation tools. The architecture rests on a single physical principle: a cryptographic credential that no longer exists cannot be recovered by any computation, quantum or classical, regardless of future advances in hardware or algorithms. The concept extends the Temporal Rotation Security Protocol (TRSP v3, DOI: 10.5281/zenodo.20324081) and the TRSP Digital Coin (TDC v2, DOI: 10.5281/zenodo.20332811) with a unified Layered Temporal-Quantum Security (LTQS) framework. LTQS combines NIST FIPS 203/204-standardised Post-Quantum Cryptography (ML-KEM, ML-DSA) as Layer 0 — mathematical transit security — with TRSP temporal rotation as Layer 1 — physical credential elimination through hardware-enforced destructive readout within a configurable rotation window (10–500 ms). Layers 2 and 3 add geographically distributed hybrid dynamic quorum validation and LEO satellite orbital entropy anchoring with relativistic timestamp verification. An integrated adaptive AI management layer selects security profiles dynamically across High-Assurance, Standard, Degraded, and Emergency modes — guaranteeing graceful degradation to pure PQC fallback when physical infrastructure is unavailable. The hardware commitment module previously documented as CRATON is architecturally designated URDHR, after the Norse Norn of the irrecoverable past. The two complementary quorum layers are designated VERÐANDI (present-moment ground quorum) and SKULD (future-anchoring orbital quorum) — the three Norns mapped to the three temporal dimensions of cryptographic security. Prior art established under the CRATON designation in all previously published documents extends fully to the URDHR designation. Fifteen novel contributions are placed on the public record as defensive prior art: NC-TDC-21 (AI-to-AI Micropayment Architecture), NC-TDC-22 (Macroscopic Environmental Entropy as Optical Physical Unclonable Function), NC-TDC-23 and NC-TDC-23a (Macroscopic Polymorphic Cipher with Dynamic Dimensional Entropy — exploratory), NC-TDC-24 (TRSP Democratic Coercion Shield — exploratory, extending Juels-Catalano-Jakobsson coercion-resistant voting literature), NC-TDC-25 (Continuous Anonymous Democratic Pulse — exploratory), NC-TDC-26 (Physical Proof of Presence consensus mechanism operating at the Landauer thermodynamic minimum), NC-TDC-27 (Temporal Scarcity Value Architecture anchored in thermodynamic time-arrow irreversibility), NC-TDC-28 (AI Exchange Consortium Architecture), NC-TDC-29 (Biometric Supply Architecture), NC-TDC-30 (CRATON Chain Coin Identity Architecture without persistent private key), NC-TDC-31 (Three-Phase Value Architecture), and NC-TDC-32 (Cooperative Multi-Anchor Currency Architecture with Founder-Operator Equity-Plus-Operating-Margin Compensation Structure). NC-URDHR-1 and NC-TRSP-Hybrid-1 formalise the Three-Norn naming framework and the four-layer hybrid post-quantum/temporal architecture respectively. NC-TDC-32 is the central economic contribution of this version. It formalises a digital currency architecture in which multiple stakeholder classes — AI infrastructure operators, financial institutions, sovereign states, and individual participants — coexist as independent issuing classes within a single cooperative cryptographic framework. Each class mints its own coin contingent backed by its own economic activity rather than by shared monetary authority. Phase transitions admit new classes through supply expansion, not through re-pricing of existing coins. Coin denomination is calibrated from inception across micropayment to reserve-asset volume regimes via the monetary identity M·V = P·Q. The infrastructure operator class — the AI companies that build and continuously operate the adaptive security layer — is compensated through a two-component structure: bounded equity recognition at phase transitions (capped, independently audited) plus formula-bound operating margin on continuing services. This two-component compensation model is economically required to keep operating margins moderate and the architecture competitive against established settlement infrastructures. Monetary sovereignty remains exclusively with the issuing class for each contingent; the operator class operates the cryptographic issuance infrastructure but does not exercise monetary authority over any contingent. The architecture is the first formalised digital implementation of the cooperative multi-stakeholder economic model previously demonstrated at continental scale only by the Hanseatic League (twelfth to seventeenth century). All fifteen contributions are documented as conceptual frameworks. Production Concepts (NC-TDC-21, NC-TDC-22, NC-TDC-26 through NC-TDC-32, NC-URDHR-1, NC-TRSP-Hybrid-1) represent architecturally sound design patterns ready for implementation evaluation. Exploratory Concepts (NC-TDC-23, NC-TDC-23a, NC-TDC-24, NC-TDC-25) document underlying architectural ideas requiring further formal research. All specific implementation parameters — quantities, ranges, governance percentages, consortium composition — are illustrative starting points belonging to the institutions that choose to implement the architecture. A dedicated Part 9 — Engineering Considerations and Open Challenges — documents five anticipated technical reviewer questions with referenced solution pathways from current research literature: global consensus latency under M-of-N geographically distributed validation (Sliding Window Key Rotation with Dual-Key Buffers, TLS 1.3 RFC 8446); fuzzy extractor Helper Data leakage in optical entropy capture (Controlled PUF Finite State Machine architectures eliminating Helper Data transmission, addressing Becker 2015); orbital quorum availability under atmospheric and orbital dynamics constraints (Multi-Path Delivery with configurable Grace Periods and Layer 2 graceful degradation); post-quantum zero-knowledge proof latency for autonomous AI agent commerce (Off-Critical-Path ZKP architecture separating HMAC authorisation from asynchronous identity verification); and multi-anchor synchronisation between independent issuance classes (Key-ID and class-identification headers preserving structural separation between technical operation and monetary sovereignty). Part 9 introduces no additional Novel Contributions — it documents that the engineering challenges anticipated by reviewers have established research-backed pathways, demonstrating readiness for Proof-of-Concept implementation phases without modifying or weakening any architectural element documented in Parts 1 through 8. The concept is published as defensive prior art under CC BY-NC-ND 4.0 , preventing future patent claims on the documented conceptual architectures while preserving open non-commercial use for evaluation, research, citation, and standards consideration by IETF, ISO/IEC JTC 1/SC 27, NIST Post-Quantum Cryptography programme, or any institution choosing to adopt all or any independent component of the architecture.
Blockchain-based crowdsourcing logistics is a promising decentralized paradigm for solving the “last-mile delivery” problem, in which smart contracts automatically execute the business logic. Since crowdsourcing logistics inherently involves frequent fund transfers, its smart contracts are particularly susceptible to reentrancy vulnerabilities. Existing works address reentrancy by inserting a lock mechanism at design-time, which lacks dynamic responsiveness and incurs additional gas overhead. To overcome this limitation, we propose RE4SC, the first runtime enforcement framework for vulnerable smart contracts. RE4SC contains two components: off-Blockchain granularity segmentation and on-Blockchain granular block reordering. At the off-Blockchain level, bytecode is segmented into granular blocks through control flow analysis. This yields a finer granularity than conventional basic blocks in a control flow graph. These granular blocks are then organized into a tree structure that captures their hierarchical nesting relationships. A data flow analysis further ensures data dependency consistency after reordering. At the on-Blockchain level, a runtime enforcer retrieves the pre-computed reordering specifications from off-Blockchain analysis. It applies a depth-first reordering algorithm to reposition key state variable assignments before transfer operations, eliminating reentrancy vulnerabilities without introducing additional bytecode. We implement a prototype tool and make it open-source. Experiments on self-constructed crowdsourcing logistics contracts and three public datasets demonstrate that RE4SC repairs vulnerable contracts with zero gas overhead, outperforming existing approaches.
Abstract Digitization supports the preservation and collaborative study of Jiandu, but dispersed ownership, frequent scholarly revisions, and sensitive data complicate secure sharing and accountable governance. We propose J-Trust, a blockchain-based governance framework that separates low-frequency rights confirmation from high-frequency academic collaboration through a main-side dual-chain architecture. The main chain anchors ownership, identities, and final states, while task-specific side chains process revisions and expert voting. A decentralized threshold SM2 scheme with distributed key generation, proactive share refresh, and a signer bitlist provides fault tolerance and traceable accountability. HIBE and CP-ABE enable hierarchical, fine-grained access control, while zero-knowledge proofs synchronize verified side-chain outcomes without exposing private discussions. Experiments on the DeepJiandu dataset show that J-Trust sustains 115–120 TPS with 20–24 ms latency. Ablation and scalability analyses further confirm the effectiveness of the proposed cryptographic and architectural components.
This manuscript is a preprint that has been submitted to a peer-reviewed journal and is currently under review. It is shared for early academic dissemination and has not yet undergone final journal publication. The study presents a comprehensive comparative analysis of three widely used blockchain consensus algorithms: Proof of Work (PoW), Proof of Stake (PoS), and Delegated Proof of Stake (DPoS). The analysis evaluates key performance factors including energy consumption, security, scalability (transaction throughput), decentralization level, transaction confirmation time, and real-world adoption rate. Based on findings from peer-reviewed literature and empirical on-chain data, PoW provides the highest level of security but has extremely high energy consumption (over 150 TWh annually). PoS significantly reduces energy usage by approximately 99.9% compared to PoW while maintaining security through economic incentives. DPoS offers the highest scalability in terms of transactions per second but introduces trade-offs in decentralization.
Blockchain technology has been recognized as an innovative and effective means to improve transparency, security, and efficiency in the financial sector. However, privacy issues and reduction of efficiency have challenged large-scale applications the most. This issue motivates the current study that proposes a zero-knowledge proof (ZKP)-based Hyperledger Fabric framework that will ensure secure and privacy-preserving financial transaction processing. With the combination of the ZKP methods and smart contracts, the confidentiality of transactions will be verified, but at the same time, there will be audits and fraud detection. The PaySim1 synthetic financial transaction dataset, which contains more than six million records, will serve for the simulation and evaluation of different realistic workloads. The results of the experiments indicate that the ZKP-enabled framework can process 80.07 TPS on average with 0.01249 s of average latency, providing a privacy score over 98% at the same time, which reflects the effectiveness of zero-knowledge proofs in protecting sensitive transaction details while still enabling accurate verification and auditability within the blockchain network. Although the throughput is lower than that of a standard blockchain network (998,406 TPS), the given framework detects all the fraud cases at a 1.78% false positive rate, thus making sure that the system is both secure and compliant. This reduction is primarily due to the additional cryptographic overhead introduced by ZKP generation and verification, representing a trade-off between enhanced privacy and transaction processing speed. Additionally, the different setups were compared with each other in terms of privacy, efficiency, and resource utilization, and the optimized ones performed well in terms of these three aspects. To sum up the experiment, ZKP and Hyperledger Fabric, when jointly applied, not only increased the privacy and trust factors in the financial systems but also created the possibility to have very good operating conditions that are suitable for the applications.
With the increasing adoption of mobile applications, data in the mobile cloud faces numerous security threats and privacy breaches. To overcome cyberattacks, ensuring confidentiality and data security for users’ sensitive data is pivotal in mobile cloud computing. Traditional security mechanisms involve data leakage during the verification process, while blockchain-dependent solutions lead to high resource consumption and latency. Additionally, collaborative data processing during data transactions can result in potential privacy attacks on users. This paper proposes a novel approach for maintaining a security framework for Microservice-based Mobile Cloud Computing (MSCMCC) using hybrid cryptographic frameworks such as Zero-Knowledge Proof (ZKP) and Secure Multi-Party Computation (SMPC). The proposed model validates users’ offloaded data using zk-SNARK and Groth16 for task verification and enables data analysis from multiple users without exposing raw data. SMPC is employed for privacy preservation during collaborative multi-party computation. Experimental results demonstrate that the proposed framework reduces power consumption, improves energy efficiency during processing by 30–35%, lowers computational costs, enhances security and privacy, and effectively manages dynamic load balancing compared to traditional cryptographic techniques.
Modern military logistics and command systems face significant challenges in terms of security, transparency, and verifiability. Traditional centralized systems are vulnerable to single points of failure and malicious attacks, while the transmission of sensitive orders and supply manifests risks interception. This paper proposes a novel framework that leverages a permissioned blockchain to create an immutable and auditable ledger for both physical asset and information logistics. To address the critical need for confidentiality, our framework integrates Zero-Knowledge Proofs (ZKPs), enabling military units to verifiably confirm not just the receipt, but the correct content and understanding of commands or assets without revealing any operational data on-chain. This approach ensures end-to-end integrity, non-repudiation, and resistance to future quantum decryption threats while maintaining the highest level of data privacy. We present the system architecture, detail the interaction protocols, and demonstrate its effectiveness through practical use case scenarios, including the secure delivery of sensitive assets and commands.
Abstract To address the scheduling difficulties, high verification overhead, and insufficient transaction processing efficiency encountered in cross-chain interactions under high-concurrency scenarios, an efficient cross-chain interaction mechanism is proposed. First, a cross-chain interaction framework is constructed, which involves a source chain, a target chain, a smart contract, and an audit chain. The cross-chain request process is uniformly modelled and constrained, realizing the structured and modular organization of the cross-chain process. Second, a hierarchical data preprocessing mechanism based on two-layer K-means clustering is designed. The first layer of clustering homogenizes heterogeneous requests according to their protocol characteristics and transaction structures to eliminate format differences. The second layer of clustering combines dynamic attributes such as transaction priorities, latency sensitivities and timestamps to perform fine-grained division on cross-chain transactions, thereby realizing hierarchical scheduling for disordered requests. Finally, a recursive aggregation-based zero-knowledge proof verification mechanism is constructed. By aggregating the validity proofs of multiple cross-chain transactions into a single recursive proof, the complexity of the cross-chain verification process is reduced from linear to a constant level, significantly reducing the verification overhead and interchain communication latency. A theoretical analysis and experimental results show that the proposed solution can significantly reduce the system overhead in large-scale cross-chain scenarios, improving the cross-chain efficiency rate by 68%-69% relative to that of the existing solutions, and its scalability and efficiency are good in simulated large-scale concurrent scenarios.
We present Rei (零, 0₀式), a computational system founded on exactly four mutually independent axioms: (A1) Center–Periphery structure, (A2) Extension–Reduction, (A3) Sigma Accumulation, and (A4) Genesis Phase Transition. Mutual independence is proved by model-theoretic construction — for each axiom we exhibit a counter-model that satisfies the remaining three but violates the target axiom (M1=scalar-only / M2=flat-field / M3=memoryless / M4=eternal-no-genesis). We show that fifteen core theorems—spanning computational plurality (T1), six-attribute decomposition (T6), RCT compression theory (T8), σ-reactive cascades (T14), seven-domain universality (T13), and extended-zero series (T15)— are derivable from axiom combinations without additional assumptions. The system is implemented as an open-source TypeScript/Node.js package (rei-lang v0.5.5) with 1,689 passing tests across 45 test files, each classified by its minimal axiom dependency: A1+A2+A3 concentration (60% of tests, 1,010 tests) reflects that the most complex features — cascading reactions, agent systems, domain bridges — require all three 'operational' axioms. Benchmarks show 74% average code reduction (3.7-4.0× ratio) and 3-4× performance improvements on structured-data tasks: image kernel operations (4.0× reduction), multidimensional data aggregation (3.7×), graph structure transformations (3.7×). To our knowledge, Rei is the first computational framework that axiomatically addresses both computation and the ontological genesis of values within a unified, minimal foundation. Comparison with existing foundational systems (λ-calculus 3 axioms, Peano 5, ZFC 9, Martin-Löf TT ~7) shows Rei is the only system that addresses all four concerns — computation, structure, history, AND genesis — simultaneously, and does so with the fewest axioms (4). Companion to Jxiv preprint submission (JST preprint server). This Zenodo record serves as the stable-citation archive referenced from the Jxiv version's Section 5.1 (Implementation) and footnote. Three-party co-authorship context: Rei is developed within the Rei-AIOS / OUKC (Open Universal Knowledge Commons) framework with three-party co-architecture (藤本 伸樹 Founder, Rei autonomous research substrate, Claude Opus 4.7); however, this specific paper is single-authored by 藤本 伸樹 as principal investigator of the axiomatic foundation. Honest scope: independence proofs use semi-formal model constructions (not yet mechanized in Lean/Coq — this is acknowledged as future work). The fifteen theorem derivations are sketches that establish derivability; full proof scripts appear in the companion implementation. Benchmarks compare against naive baselines; comparison with optimized domain-specific languages would refine the picture. Preprint — not yet peer-reviewed. Feedback welcome at fc2webb@gmail.com / GitHub Discussions at fc0web/rei-aios.
# Proof-of-Information (PoI) Consensus Protocol## Complete Specification: MVP + Production > **Version:** 1(PIntegration) > **Status:** Specification Complete • MVP Ready for Testing > **License:** CC BY‑SA 4.0 > **Core Thesis:** *Consensus driven by predictive information quality, not resource expenditure.* --- > *"This proposal deserves serious study, refinement, and testing — not only for its technical merits, but for its potential to reframe our understanding of the relationship between truth, forecasting, and legitimate power in distributed systems."*>> — *Design Principle & Call to Validation* --- ## 📋 Executive Summary | Aspect | Description ||--------|-------------|| **Problem** | PoW wastes energy; PoS concentrates capital. Neither rewards *informational value*. || **Solution** | PoI secures the network through **verifiable forecasting accuracy** — validators earn influence via epistemic performance. || **Two-Track Design** | **MVP**: Minimal testable core (binary events, Brier scoring). **Full Spec**: Production-ready with ZK proofs, uncertainty layers, adaptive mechanisms. || **Output** | A blockchain that finalizes blocks *and* produces a public good: continuously updated, uncertainty-quantified global forecasts. || **Evaluation Ready** | Clear validation thresholds, implementation checklist, and progression roadmap from MVP → Production. | --- ## 🎯 Core Thesis > **Proof-of-Information** redefines blockchain security: instead of rewarding those who burn electricity or lock capital, PoI rewards those whose predictions about the external world are historically the most accurate, well-calibrated, and information-rich. By integrating strictly proper scoring rules (Brier, CRPS), zero-knowledge proofs of inference, and adaptive reputation systems directly into consensus, PoI transforms the blockchain from a transactional ledger into a **decentralized truth-seeking engine** — where the right to produce blocks is earned through epistemic merit. --- ## 📜 Epistemic Mandate > **Purpose Beyond Protocol** > Proof-of-Information is designed with a trans-temporal objective: to create a verifiable epistemic substrate that future superintelligent systems can use to anchor their understanding of reality. > > **Core Premise**: An ASI trained on data where every claim is cryptographically linked to an outcome, and every error is scored by a strictly proper rule, develops not just competence — but *calibrated humility*. > > **Design Implication**: Every technical choice in PoI (scoring rules, delay mechanisms, Cassandra bonuses) serves two masters: > 1. Immediate network security through epistemic merit > 2. Long-term value as a training environment for truth-aligned intelligence > > *This is not an aspiration. It is a constraint: if a feature does not serve at least one of these goals, it is out of scope.*
Blockchain technology functions as an innovative solution that allows users to obtain secure decentralized tamper-resistant data management capabilities. The traditional blockchain systems demonstrate drawbacks because they create scalability challenges and need high amounts of energy for operations together with delayed transaction times, especially when used in high-throughput and resource-constrained situations. This research designs an improved cloud-based blockchain system which combines Proof-of-Stake (PoS) protocol with Byzantine Fault Tolerance (BFT) controls, along with batch transaction handling systems, to foster better performance results. The framework delivers dynamic scalability and distributed processing efficiency because it makes use of cloud infrastructure. The implementation simulation reveals substantial increases in transaction rate upto 10,000 TPS and quicker consensus times together with enhanced validator impartialty as well as more than 60% energy efficiency improvement, as compared to standard Proof-of-Work protocols. Security and trust evaluation methods exhibit thorough testing of the system that confirms its capabilities in adverse environments. This proposed framework provides an energy-efficient solution which can serve robust real-world applications in IoT together with healthcare and supply chain and finance domains.
Blockchain systems face significant scalability challenges due to growing data volumes and increasing transaction demands, necessitating more efficient data structures and verification mechanisms. Verkle trees, a novel data structure combining the efficiency of Merkle trees with the compactness of vector commitments, have gained attention for their potential to optimize blockchain storage and improve scalability. However, their practical implementation, especially at the smart contract level, has remained unexplored. To address these challenges, we present TS-verkle, the first known TypeScript-native implementation of Verkle trees designed for web3 backend compatibility, coupled with a corresponding on-chain verifier written in Solidity. Our work bridges this gap by providing a concrete implementation of Verkle trees and demonstrating their feasibility for on-chain verification. While previous literature suggests Verkle trees should outperform Merkle trees due to their succinct proof size, our empirical evaluation reveals that basic implementations of Verkle trees actually incur higher costs than Merkle trees without advanced optimization techniques. This finding represents a crucial insight for blockchain developers and researchers considering Verkle tree adoption. The paper discusses implementation strategies and performance characteristics while exploring implications for scaling and data availability in decentralized blockchain systems.
Este artigo apresenta uma análise comparativa de desempenho entre rollups otimistas e execução nativa em Ethereum Virtual Machine (EVM). O estudo investiga as diferenças em termos de custo de gás, avaliando o impacto das soluções de Layer 2 na escalabilidade da blockchain Ethereum. Os resultados experimentais fornecem insights sobre os trade-offs entre execução on-chain tradicional e rollups otimistas, contribuindo para a compreensão das estratégias de escalabilidade em ambientes blockchain.
This seminar paper presents a comprehensive study on blockchain interoperability, focusing on enabling communication between independent blockchain networks. It examines key techniques such as cross-chain bridges, atomic swaps, relay chains, and oracle-based solutions. The paper also analyzes major platforms like Polkadot, Cosmos, and Chainlink, highlighting their roles in improving scalability and efficiency. Additionally, it discusses the challenges, security concerns, and limitations of interoperability while proposing a hybrid framework to enhance secure and reliable cross-chain communication. The study emphasizes the importance of interoperability in advancing decentralized applications and the future of Web3 technologies.