The integration of cryptocurrencies into institutional portfolios necessitates the adoption of robust risk modeling frameworks. This study is a part of a series of subsequent works to fine-tune model risk analysis for cryptocurrencies. Through this first research work, we establish a foundational benchmark by applying the traditional industry-standard Geometric Brownian Motion (GBM) model. Popularly used for non-crypto financial assets, GBM assumes Lognormal return distributions for a multi-asset cryptocurrency portfolio (XRP, SOL, ADA). This work utilizes Maximum Likelihood Estimation and a correlated Monte Carlo Simulation incorporating the Cholesky decomposition of historical covariance. We present our stock portfolio model as a Minimum Variance Portfolio (MVP). We observe the model's structural shift within the heavy-tailed, non-Gaussian cryptocurrency environment. The results reveal limitations of the Lognormal assumption: the calculated Value-at-Risk at the 5% confidence level over the one-year horizon. For baselining our results, we also present a holistic comparative analysis with an equity portfolio (AAPL, TSLA, NVDA), demonstrating a significantly lower failure rate. This performance provides conclusive evidence that the GBM model is fundamentally the perfect benchmark for our subsequent works. Results from this novel work will be an indicator for the success criteria in our future model for crypto risk management, rigorously motivating the development and application of advanced models.
Firmware integrity is a foundational requirement for securing Cyber-Physical Systems (CPS), where malicious or compromised firmware can result in persistent backdoors, unauthorized control, or catastrophic system failures. Traditional verification mechanisms such as secure boot, digital signatures, and centralized hash databases are increasingly inadequate due to risks from insider threats and single points of failure. In this paper, we propose a decentralized firmware integrity verification framework built on the Ethereum blockchain, offering tamper-proof, transparent, and trustless validation. Our system stores SHA-256 hashes of firmware binaries within smart contracts deployed on the Ethereum Sepolia testnet, using Web3 and Infura for seamless on-chain interaction. A Python-based client tool computes firmware hashes and communicates with the blockchain to register and verify firmware authenticity in real-time. We implement and evaluate a fully functional prototype using real firmware samples, demonstrating successful contract deployment, hash registration, and integrity verification through live blockchain transactions. Experimental results confirm the reliability and low cost (in gas fees) of our approach, highlighting its practicality and scalability for real-world CPS applications. To enhance scalability and performance, we discuss extensions using Layer-2 rollups and off-chain storage via the InterPlanetary File System (IPFS). We also outline integration pathways with secure boot mechanisms, Trusted Platform Module (TPM)-based attestation, and zero-trust architectures. This work contributes a practical and extensible model for blockchain-based firmware verification, significantly strengthening the defense against firmware tampering and supply chain attacks in critical CPS environments.
Martin Perešíni, Tomáš Hladký, Jakub Kubík, Ivan Homoliak
The aim of this work is to enhance blockchain security by deepening the understanding of selfish mining attacks in various consensus protocols, especially the ones that have the potential to mitigate selfish mining. Previous research was mainly focused on a particular protocol with a single selfish miner, while only limited studies have been conducted on two or more attackers. To address this gap, we proposed a stochastic simulation framework that enables analysis of selfish mining with multiple attackers across various consensus protocols. We created the model of Proof-of-Work (PoW) Nakamoto consensus (serving as the baseline) as well as models of two additional consensus protocols designed to mitigate selfish mining: Fruitchain and Strongchain. Using our framework, thresholds reported in the literature were verified, and several novel thresholds were discovered for 2 and more attackers. We made the source code of our framework available, enabling researchers to evaluate any newly added protocol with one or more selfish miners and cross-compare it with already modeled protocols.
Trust between entities in any scenario without a trusted third party is very difficult, and trust is exactly what blockchain aims to bring into the digital world with its basic features. Many applications are moving to blockchain adoption, enabling users to work in a trustworthy manner. The early generations of blockchain have a problem; they cannot share information with other blockchains. As more and more entities move their applications to the blockchain, they generate large volumes of data, and as applications have become more complex, sharing information between different blockchains has become a necessity. This has led to the research and development of interoperable solutions allowing blockchains to connect together. This paper discusses a few blockchain platforms that provide interoperable solutions, emphasising their ability to connect heterogeneous blockchains. It also discusses a case study scenario to illustrate the importance and benefits of using interoperable solutions. We also present a few topics that need to be solved in the realm of interoperability.
Learning and Employment Record (LER) systems are emerging as critical infrastructure for securely compiling and sharing educational and work achievements. Existing blockchain-based platforms leverage verifiable credentials but typically lack automated skill-credential generation and the ability to incorporate unstructured evidence of learning. In this paper,a privacy-preserving, AI-enabled decentralized LER system is proposed to address these gaps. Digitally signed transcripts from educational institutions are accepted, and verifiable self-issued skill credentials are derived inside a trusted execution environment (TEE) by a natural language processing pipeline that analyzes formal records (e.g., transcripts, syllabi) and informal artifacts. All verification and job-skill matching are performed inside the enclave with selective disclosure, so raw credentials and private keys remain enclave-confined. Job matching relies solely on attested skill vectors and is invariant to non-skill resume fields, thereby reducing opportunities for screening bias.The NLP component was evaluated on sample learner data; the mapping follows the validated Syllabus-to-O*NET methodology,and a stability test across repeated runs observed <5% variance in top-ranked skills. Formal security statements and proof sketches are provided showing that derived credentials are unforgeable and that sensitive information remains confidential. The proposed system thus supports secure education and employment credentialing, robust transcript verification,and automated, privacy-preserving skill extraction within a decentralized framework.
We introduce Auditable Proof-of-Work (APoW), a novel proof-of-work (PoW) construction inspired by Hashcash-style nonce searching, which enables the auditing of other miners' work through accountable re-scanning of the nonce space. The proposed scheme allows a miner to probabilistically attest to having searched specified regions of the nonce space in earlier mining rounds, while concurrently earning rewards for performing productive work for a new block or pool share. This capability enables miners belonging to a mining pools to audit another miner's claimed effort retroactively, thereby allowing the probabilistic detection of block withholding attacks (BWAs) without requiring trusted hardware or trusted third parties. As a consequence, the construction supports the design of decentralized mining pools in which work attribution is verifiable and withholding incentives are substantially reduced. The scheme preserves the fundamental properties of conventional PoW, including public verifiability and difficulty adjustment, while adding an orthogonal auditability layer tailored to pool-based mining. Finally, while a full deployment of APoW in Bitcoin would require a consensus rule change and minor modifications to mining ASICs, the construction remains practically useful even without consensus changes, for instance, as a pool-level auditing mechanism that enables verifiable pay-for-auditing using existing pool reserves.
The rigorous security model of Bitcoin's UTXO architecture often comes at the cost of developer usability, forcing a reliance on manual stack manipulation that leads to critical financial vulnerabilities like signature malleability, unspendable states and unconstrained execution paths. Industry standards such as Miniscript provide necessary abstractions for policy verification but do not model the full imperative logic required for complex contracts, leaving gaps in state management and resource liveness. This paper introduces Bithoven, a high-level language designed to bridge the gap between expressiveness and formal safety. By integrating a strict type checker and a resource liveness analyzer with a semantic control-flow analyzer, Bithoven eliminates major categories of consensus and logic defects defined in our fault model prior to deployment. Our results indicate that this safety comes at modest cost: Bithoven compiles to Bitcoin Script with efficiency comparable to hand-optimized code, demonstrating that type-safe, developer-friendly abstractions are viable even within the strict byte-size constraints of the Bitcoin blockchain.
The rapid proliferation of Internet of Things (IoT) technologies, projected to exceed 30 billion interconnected devices by 2030, has significantly escalated the complexity of cybersecurity challenges. This survey aims to provide a comprehensive analysis of vulnerabilities, threats, and defense mechanisms, specifically focusing on the integration of network and application layers within real-time monitoring and decision-making systems. Employing an integrative review methodology, 59 scholarly articles published between 2009 and 2024 were selected from databases such as IEEE Xplore, ScienceDirect, and PubMed, utilizing keywords related to IoT vulnerabilities and security attacks. Key findings identify critical threat categories, including sensor vulnerabilities, Denial-of-Service (DoS) attacks, and public cloud insecurity. Conversely, the study highlights advanced defense approaches leveraging Artificial Intelligence (AI) for anomaly detection, Blockchain for decentralized trust, and Zero Trust Architecture (ZTA) for continuous verification. This paper contributes a novel five-layer IoT model and outlines future research directions involving quantum computing and 6G networks to bolster IoT ecosystem resilience.
We propose the Consensus-Based Privacy-Preserving Data Distribution (CPPDD) framework, a lightweight and post-setup autonomous protocol for secure multi-client data aggregation. The framework enforces unanimous-release confidentiality through a dual-layer protection mechanism that combines per-client affine masking with priority-driven sequential consensus locking. Decentralized integrity is verified via step (sigma_S) and data (sigma_D) checksums, facilitating autonomous malicious deviation detection and atomic abort without requiring persistent coordination. The design supports scalar, vector, and matrix payloads with O(N*D) computation and communication complexity, optional edge-server offloading, and resistance to collusion under N-1 corruptions. Formal analysis proves correctness, Consensus-Dependent Integrity and Fairness (CDIF) with overwhelming-probability abort on deviation, and IND-CPA security assuming a pseudorandom function family. Empirical evaluations on MNIST-derived vectors demonstrate linear scalability up to N = 500 with sub-millisecond per-client computation times. The framework achieves 100% malicious deviation detection, exact data recovery, and three-to-four orders of magnitude lower FLOPs compared to MPC and HE baselines. CPPDD enables atomic collaboration in secure voting, consortium federated learning, blockchain escrows, and geo-information capacity building, addressing critical gaps in scalability, trust minimization, and verifiable multi-party computation for regulated and resource-constrained environments.
Digital identity verification often forces a privacy trade-off, where users must disclose sensitive personal data to prove simple eligibility criteria. As blockchain applications integrate with regulated environments, this over-disclosure creates significant risks of data breaches and surveillance. This work proposes a general Selective Disclosure Framework built on Ethereum, designed to decouple attribute verification from identity revelation. By utilizing client-side zk-SNARKs, the framework enables users to prove specific eligibility predicates without revealing underlying identity documents. We present a case study, ZK-Compliance, which implements a functional Grant, Verify, Revoke lifecycle for age verification. Preliminary results indicate that strict compliance requirements can be satisfied with negligible client-side latency (< 200 ms) while preserving the pseudonymous nature of public blockchains.
Bitcoin's block reward is scheduled to decline to zero, raising concerns about whether the network can remain secure once miners rely solely on transaction fees. This paper seeks to identify the conditions under which large-scale and persistent deviation from honest mining can arise. We analyze and compare the payoffs of honest and deviating miners in a sequential decision model, and identify a deviation threshold $G_t$ at which honest mining ceases to be privately optimal. Around the 2024 Bitcoin halving, we show that current mining behavior does not exhibit large-scale or structural deviation. However, when the block reward is removed, the $G_t$ criterion implies that deviation can arise even with a very small fraction of transaction fees. Finally, we evaluate three protocol-level mechanisms: Base Fee, Fee Floor, and an adaptive maximum block size rule, and show that their combination raises the deviation threshold and mitigates incentive breakdown in a fee-only regime. These results provide a practical benchmark for assessing Bitcoin's security as block rewards disappear.
Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-off between convenience and privacy by routing sensitive financial data through centralized servers, increasing security risks, and diminishing user trust. This study introduces a device-native autonomous Artificial Intelligence (AI) agent system for privacy-preserving negotiations. The proposed system operates exclusively on user hardware, enabling real-time bargaining while maintaining sensitive constraints locally. It integrates zero-knowledge proofs to ensure privacy and employs distilled world models to support advanced on-device reasoning. The architecture incorporates six technical components within an agentic AI workflow. Agents autonomously plan negotiation strategies, conduct secure multi-party bargaining, and generate cryptographic audit trails without exposing user data to external servers. The system is evaluated in insurance and B2B procurement scenarios across diverse device configurations. Results show an average success rate of 87%, a 2.4x latency improvement over cloud baselines, and strong privacy preservation through zero-knowledge proofs. User studies show 27% higher trust scores when decision trails are available. These findings establish a foundation for trustworthy autonomous agents in privacy-sensitive financial domains.
The tokenization of real-world assets (RWAs) has emerged as a transformative application of blockchain technology, with market projections estimating trillions of dollars in tokenized assets within the coming decade. However, a fundamental challenge remains unaddressed: physical assets such as precious metals, stored commodities, and warehoused goods incur structural negative carry -- custody, insurance, and audit costs that accumulate over time. While existing tokenization models have successfully established the market for digital gold and treasuries, they typically manage operational costs at the issuer level. The FRS introduces a framework to bring these economics directly on-chain, avoiding mechanisms such as token rebasing that compromise fungibility and composability with decentralized finance (DeFi) protocols. This paper proposes the Fungible Reserve Standard (FRS), a deterministic token design framework that encodes carrying costs transparently into on-chain logic. The FRS introduces an asset-per-token variable q(t) that decreases according to a predefined annualized carrying cost rate, coupled with a supply reconciliation mechanism that preserves holder balances and ERC-20 composability. While mathematically inspired by the daily expense ratio accrual in traditional asset management -- which often embed centralized profit margins -- the FRS design specifically encodes actual operational carrying costs to provide pure institutional-grade accounting clarity without compromising DeFi compatibility. The framework is asset-agnostic and applicable to any real-world asset with positive, predictable holding costs.
Bitcoin's limited programmability and transaction throughput have historically prevented native Bitcoin from participating in decentralized finance (DeFi) applications. Existing solutions depend on honest-majority thresholds, or centralized custodial entities that introduce significant trust requirements. This paper introduces Bitcoin Smart Accounts (BSA), a novel protocol that enables native Bitcoin to access DeFi through trust-minimized infrastructure while maintaining self-custody of funds. BSA achieves this through a combination of emulated Bitcoin covenants using Partially Signed Bitcoin Transactions (PSBTs) and Taproot scripts, a Trusted Execution Environment (TEE)-based arbitration system, and destination chain smart contracts that enable DeFi platforms to accept self-custodial Bitcoin as collateral without necessitating protocol-level modifications. The setup leverages liquidity secured by the Lombard Security Consortium which provides a twofold advantage: for a DeFi protocol, liquidators rely on fungible assets with deep liquidity to quickly exit positions, while for a depositor, the general trust assumptions of honest majority (m-of-n) are reduced to existential honesty (1-of-k). We present the complete protocol design, including the Bitcoin architecture, the TEE-based arbitration mechanism, and the Smart Account Registry for protocol management. We provide a security analysis that demonstrates the correctness, safety, and availability properties under our trust model. Our design enables native Bitcoin to serve as collateral in lending markets and other DeFi protocols without requiring users to relinquish custody of funds.
Marco Dessalvi, Massimo Bartoletti, Alberto Lluch-Lafuente
Decentralized Finance (DeFi) has revolutionized financial markets by enabling complex asset-exchange protocols without trusted intermediaries. Automated Market Makers (AMMs) are a central component of DeFi, providing the core functionality of swapping assets of different types at algorithmically computed exchange rates. Several mainstream AMM implementations are based on the constant-product model, which ensures that swaps preserve the product of the token reserves in the AMM - up to a trading fee used to incentivize liquidity provision. Trading fees substantially complicate the economic properties of AMMs, and for this reason some AMM models abstract them away in order to simplify the analysis. However, trading fees have a non-trivial impact on users' trading strategies, making it crucial to develop refined AMM models that precisely account for their effects. In this work, we extend a foundational model of AMMs by introducing a new parameter, the trading fee ϕ ∈ (0,1], into the swap rate function. Fee amounts increase inversely proportional to ϕ. When ϕ = 1, no fee is applied and the original model is recovered. We analyze the resulting fee-adjusted model from an economic perspective. We show that several key properties of the swap rate function, including output-boundedness and monotonicity, are preserved. At the same time, other properties - most notably additivity - no longer hold. We precisely characterize this deviation by deriving a generalized form of additivity that captures the effect of swaps in the presence of trading fees. In particular, we prove that when ϕ < 1, executing a single large swap yields strictly greater profit than splitting the trade into smaller ones. Finally, we derive a closed-form solution to the arbitrage problem in the presence of trading fees and prove its uniqueness. All results are formalized and machine-checked in the Lean 4 proof assistant.
Non-fungible tokens (NFTs) on Ethereum currently follow a binary mobility paradigm: ERC-721 enables unrestricted transfers, whereas SBTs (ERC-5192) prohibit transfers entirely. We identify a design gap in which no standard mechanism supports bounded transferability, where ownership mobility is allowed but limited to a finite number of programmable transfers. We study counted NFT transfers and introduce ERC-7634 as a minimal realization compatible with ERC-721. The design augments each token with a transfer counter and configurable cap L, allowing ownership to evolve under a finite transfer budget. ERC-7634 defines a minimal extension interface with three lightweight functions (transferCountOf, setTransferLimit, and transferLimitOf), two events, and native-transfer hooks, requiring fewer than 60 additional lines of Solidity while preserving full backward compatibility with existing NFT infrastructure. We analyze behavioral and economic consequences of counted transfers. Our results reveal (i) a mobility premium induced by remaining transfer capacity, (ii) a protocol-level costing signal that can deter wash trading in cap-aware markets through irreversible budget consumption, (iii) bounded recursive collateralization enabled by limited ownership turnover, and (iv) associated security and gas-cost implications, including wrapper-bypass trade-offs. Evaluation on calibrated simulations shows that moderate limits (e.g., L = 10) affect fewer than 15% of tokens under representative transfer distributions, while repeated manipulation becomes unprofitable after a few cycles in a cap-aware pricing model; the additional gas overhead remains below 11% per transfer. We further position ERC-7634 within the NFT mobility design space, derive practical cap-selection guidelines, and discuss post-cap ownership outcomes including soulbound conversion, auto-burn, and provenance freeze.
Blockchain as a promising technology is gaining its popularity ever since proof-of-work based Bitcoin came to the world. Nevertheless, Bitcoin achieves consensus at an expensive cost of energy. Proof-of-stake is one of the solutions for such a problem. Participants of PoS protocols achieve dynamic-availability in permissionless settings. Parties can join and leave the protocol at their will without notifying others. However, such protocol relies heavily on a central clock, providing the function of synchrony by collecting the finish status of every honest participant. In our protocol, the global function maintains the round information for each participant no longer needed. We analyze and modify the round into real-time based round model. Message delivery delay is also taken into consideration of the round length. However, participant need the connection of a real-world time global clock which is crucial to calculate the current round. And round length also is adjusted due to the changing network situation at the start of every new epoch.
Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face challenges in scalability, security, and update validation. We propose ZK-HybridFL, a secure decentralized FL framework that integrates a directed acyclic graph (DAG) ledger with dedicated sidechains and zero-knowledge proofs (ZKPs) for privacy-preserving model validation. The framework uses event-driven smart contracts (EDSCs) and an oracle-assisted sidechain to verify local model updates without exposing sensitive data. A built-in challenge mechanism efficiently detects adversarial behavior. In experiments on image classification and language modeling tasks, ZK-HybridFL achieves faster convergence, higher accuracy, lower perplexity, and reduced latency compared to Blade-FL and ChainFL. It remains robust against substantial fractions of adversarial and idle nodes, supports sub-second on-chain verification with efficient gas usage, and prevents invalid updates and orphanage-style attacks. This makes ZK-HybridFL a scalable and secure solution for decentralized FL across diverse environments.
There have been various attempts at token standards on numerous blockchain platforms today to fundamentally change the way assets are traded in the traditional capital markets, but there is a lack of research and resolution on regulatory issues that become the common foundation for interoperability and reusable standards. Our proposal, Regulatory Compliance Protocol (RCP), is based on the regulations and reports of 15 global financial institutions and standardizes recommendations and guidelines involving the overall asset tokenization of TradFi and DeFi into five regulatory groups: Traceability, Privacy, Enforceability, Finality and Tokenizability, compiling them into 31 items and presenting a benchmark for technology and standards as an underlying protocol. To review the legality and effectiveness of RCP, it was validated based on three tokenization and trading scenarios, and by benchmarking existing asset-tokenization standards (ERC-20, ERC-7943, ERC-1400, and ERC-3643) against RCP, it makes explicit which regulatory requirements each standard addresses at the token level and which remain inherently off-chain.
Public distributed ledgers enforce integrity through radical transparency, creating tension with data minimization principles required for regulatory compliance. While Zero-Knowledge Proofs (ZKPs) offer a theoretical privacy solution, existing constructions often overlook adversarial constraints in smart contract environments. Specifically, the asynchronous decoupling of off-chain proof generation from on-chain submission introduces front-running and proof-reuse risks in public mempools. In this work, we formalize Selective Disclosure Authorization Schemes (SDAS), a cryptographic primitive for granular and revocable compliance checks on public ledgers without revealing the underlying witness. We define a security model for SDAS, introducing Ledger-Bound Attribute Unlinkability and Context-Aware Sender Binding to capture how valid proofs remain bound to their intended authorization context. To validate sender binding, we present ZK-Compliance, an Ethereum-based instantiation that operationalizes a user-controlled "Grant, Verify, Revoke" lifecycle. We implement the sender-binding component using a 14-constraint Circom circuit that anchors the zero-knowledge proof to the executing on-chain sender address. Our Sepolia evaluation confirms practical viability: browser-based proof generation executes in under 200 ms, and on-chain verification costs 240,512 gas, neutralizing proof reuse by different callers while preserving strict attribute privacy.
Traditional facility management often relies on centralized decision-making structures that limit stakeholder participation, leading to misalignment with occupant needs and reduced satisfaction. This paper proposes a novel blockchain- and Decentralized Autonomous Organization (DAO)-based framework for community-based facilities management in smart buildings. The framework comprises two key components: a decentralized governance platform that facilitates transparent collective decision-making through blockchain-based voting, and a maintenance management platform with an incentivization mechanism that encourages building occupants to actively contribute to facility upkeep through tokenized rewards. System evaluation includes cost analysis, scalability, data security considerations, usability testing, and semi-structured interviews with facility managers and researchers to assess the platform's usefulness, challenges, and adoption potential. The findings demonstrate the framework's potential as a viable incentivization solution for engaging stakeholders in the collective upkeep and improvement of building infrastructure.
ABSTRACT Blockchain technology has emerged as a foundational infrastructure for decentralized applications, where consensus protocols play a critical role in ensuring security, consistency and trust among distributed participants. This paper presents a comprehensive comparative analysis of nine widely adopted consensus protocols across public (proof of work [PoW], proof of stake [PoS], delegated proof of stake [DPoS]) and private (practical Byzantine fault tolerance [PBFT, Raft, Kafka, proof of elapsed time [PoET], yet another consensus [YAC], Paxos) blockchain systems. Unlike prior surveys, this work integrates workflow‐level operational modelling, quantitative performance comparison and application‐driven decision support within a unified analytical framework. Our analysis shows that PoW achieves strong decentralization at 3–15 transactions per second (TPS) with 10–60 min finality, whereas PoS improves throughput to tens to thousands of TPS with seconds‐to‐minutes finality while reducing energy consumption by approximately 99%. Private blockchain protocols such as PBFT and Kafka achieve sub‐second latency and throughput exceeding 10,000 TPS by trading off decentralization for performance and control. Furthermore, we develop a decision‐support framework that maps consensus mechanisms to application requirements and provides a critical synthesis of security risks, scalability limitations, and emerging solutions. The findings highlight that no single protocol satisfies all design goals, reinforcing the necessity of context‐aware consensus selection.
Qian'ang Mao, Jiaxin Wang, Liu Ya, Li Zhu · 6 authors
The decentralized architecture of Web3 technologies creates fundamental challenges for Anti-Money Laundering and Counter-Financing of Terrorism compliance. Traditional regulatory technology solutions designed for centralized financial systems prove inadequate for blockchain's transparent yet pseudonymous networks. This systematization examines how blockchain-native RegTech solutions leverage distributed ledger properties to enable novel compliance capabilities. We develop three taxonomies organizing the Web3 RegTech domain: a regulatory paradigm evolution framework across ten dimensions, a compliance protocol taxonomy encompassing five verification layers, and a RegTech lifecycle framework spanning preventive, real-time, and investigative phases. Through analysis of 41 operational commercial platforms and 28 academic prototypes selected from systematic literature review (2015-2025), we demonstrate that Web3 RegTech enables transaction graph analysis, real-time risk assessment, cross-chain analytics, and privacy-preserving verification approaches that are difficult to achieve or less commonly deployed in traditional centralized systems. Our analysis reveals critical gaps between academic innovation and industry deployment, alongside persistent challenges in cross-chain tracking, DeFi interaction analysis, privacy protocol monitoring, and scalability. We synthesize architectural best practices and identify research directions addressing these gaps while respecting Web3's core principles of decentralization, transparency, and user sovereignty.
The real estate sector remains highly dependent on manual document handling and verification, making processes inefficient and prone to fraud. This work presents a system that integrates optical character recognition (OCR), natural language processing (NLP), and verifiable credentials (VCs) to automate document extraction, verification, and management. The approach standardizes heterogeneous document formats into VCs and applies automated data matching to detect inconsistencies, while the blockchain provides a decentralized trust layer that reinforces transparency and integrity. A prototype was developed that comprises (i) an OCR-NLP extraction pipeline trained on synthetic datasets, (ii) a backend for credential issuance and management, and (iii) a frontend supporting issuer, holder, and verifier interactions. Experimental results show that the models achieve competitive accuracy across multiple document types and that the end-to-end pipeline reduces verification time while preserving reliability. The proposed framework demonstrates the potential to streamline real estate transactions, strengthen stakeholder trust, and enable scalable, secure digital processes.