Blockchain Papers

Bora Buğra Sezer, Sedat Akleylek

No abstract is available for this record.

Omar Dib, Shiyun Li, Zhengkun Li, Rouwaida Abdallah · 5 authors

Federated Learning (FL) offers a promising paradigm for privacy-preserving collaborative training, yet it remains highly vulnerable to adversarial behaviors, client unreliability, and challenges associated with non-independent and identically distributed (non-IID) data. Existing secure aggregation techniques, while preserving confidentiality, fail to guarantee the integrity and trustworthiness of model updates, leaving FL deployments exposed to poisoning and consistency attacks. This work introduces FL-SMPC++, a robust and privacy-preserving FL framework designed to address these challenges. The primary objective is to develop a scalable solution that ensures verifiable, privacy-preserving aggregation while mitigating malicious client behaviors, dropouts, and data heterogeneity. Our approach integrates Secure Multi-Party Computation (SMPC), Pedersen commitments, and zero-knowledge proofs (ZKPs) to cryptographically bind clients' submitted updates to their validation outcomes without revealing private data. We propose a dynamic client selection strategy based on shared validation performance, a dropout-tolerant threshold aggregation protocol, and a warm-up initialization phase to counteract non-IID distributions. Comprehensive experiments on MNIST, CIFAR-10, FEMNIST, and UCI Heart Disease show that FL-SMPC++ consistently outperforms FedAvg, FedProx, and FedNova. For example, under a label-flipping attack with 30% malicious clients on CIFAR-10 (non-IID), FL-SMPC++ achieves 78.9% accuracy compared to 67.4% for FedAvg, representing an absolute gain of 11.5%. Across datasets, the framework limits accuracy degradation to 6–8% under attack, while baselines suffer 13–20% losses. These results demonstrate that FL-SMPC++ achieves strong cryptographic privacy guarantees together with empirically validated resilience and convergence, offering a scalable and practical blueprint for trustworthy FL in adversarial and resource-constrained environments. • A novel FL framework combines SMPC, commitments, and zero-knowledge proofs. • Ensures submitted model updates match validated ones without revealing them. • Uses dynamic validation for secure and fair client selection. • Tolerates client dropouts using a threshold-based aggregation mechanism. • Outperforms baseline FL methods under adversarial and non-IID conditions.

Iqra Nazir, Nermish Mushtaq, Waqas Amin

The smart grid (SG) plays a seminal role in the modern energy landscape by integrating digital technologies, the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) to enable bidirectional energy flow, real-time monitoring, and enhanced operational efficiency. However, these advancements also introduce critical challenges related to data privacy, cybersecurity, and operational balance. This review critically evaluates SG systems, beginning with an analysis of data privacy vulnerabilities, including Man-in-the-Middle (MITM), Denial-of-Service (DoS), and replay attacks, as well as insider threats, exemplified by incidents such as the 2023 Hydro-Québec cyberattack and the 2024 blackout in Spain. The review further details the SG architecture and its key components, including smart meters (SMs), control centers (CCs), aggregators, smart appliances, and renewable energy sources (RESs), while emphasizing essential security requirements such as confidentiality, integrity, availability, secure storage, and scalability. Various privacy preservation techniques are discussed, including cryptographic tools like Homomorphic Encryption, Zero-Knowledge Proofs, and Secure Multiparty Computation, anonymization and aggregation methods such as differential privacy and k-Anonymity, as well as blockchain-based approaches and machine learning solutions. Additionally, the review examines pricing models and their resolution strategies, Demand–Supply Balance Programs (DSBPs) utilizing optimization, game-theoretic, and AI-based approaches, and energy storage systems (ESSs) encompassing lead–acid, lithium-ion, sodium-sulfur, and sodium-ion batteries, highlighting their respective advantages and limitations. By synthesizing these findings, the review identifies existing research gaps and provides guidance for future studies aimed at advancing secure, efficient, and sustainable smart grid implementations.

Sandeep Kumar Mathariya, Sai Priyanka Tirumalasetty, Ajit More, P. Dinesh Kumar · 6 authors

As more and more forensic investigations use blockchain technology to preserve digital data, we will need to build systems that are both contextually optimal for investigations and impossible to break into. Most current blockchain forensic frameworks have a monolithic approach to consensus and static assessment models. This makes them not ideal for the ever-changing forensic context of different event sensitivity, legality, and auditability needs. There are currently barriers to the successful application in high-stakes forensic environments. This work presents the Forensic-Driven Blockchain Evaluation and Simulation Architecture (ForBESA), an extensive simulation-based evaluation framework designed to compare Proof-of-Stake (PoS), Directed Acyclic Graph (DAG), and Byzantine Fault Tolerant (BFT) blockchains against forensic key performance indicators (KPIs) to address existing deficiencies. This framework has five new modules. The Context-Aware KPI Weighting Engine (CAKWE) first makes dynamic KPI weight vector creation by using forensic event metadata and a decision tree classifier. Second, the Temporal Provenance DAG Tracker (TPDT) makes richer DAGs by adding investigator metadata and transaction timings. This makes it easier to find traces in the future. Third, the Hybrid Ledger Simulation Module will simulate how evidence moves between Hyperledger, IOTA, and Ethereum 2.0 using different KPIs that take forensic factors into account. Fourth, the Performance-Forensic Tradeoff Analyzer (PFTA) employs Pareto analysis and utility-based optimization to figure out if a design is good enough by weighing the pros and cons of performance and forensic depth. Finally, the Chain-of-Custody Cryptographic Verifier (C3V) combines smart contracts and zero-knowledge proofs to make sure that the evidence is safe and can be used in court. The experiments hardly show that the forensic efficacy has improved, that trace reconstruction is accurate to 98.1 %, and that tampering is detected 100% of the time. This study presents the inaugural paradigm for forensic-aware blockchain evaluation. The system enables ongoing digital investigations that are precise, legally compliant, and contextually aware.

Manisha Sengupta

Fintech modernization is a ground-up shift from traditional batch-processing infrastructure to event-driven real-time architectures that redefine financial service delivery and social mechanisms of trust. Modern financial institutions draw on advanced stream processing technologies, API-first integration, and distributed computing to support transaction throughput rates in millions of operations per second with sub-millisecond latencies for key financial transactions. Occasion-driven architectures (also known as event-driven architecture) provide instantaneous affirmation of transactions, real-time detection of fraud, and clear audit trails through immutable event recording structures that ensure end-to-end transaction traceability for regulatory purposes. Mobile-first design patterns and modern web-based packages boost access to finance for the underprivileged through offline-enabled interfaces that function across diverse device specifications and network connectivity eventualities. Advanced cryptographic algorithms, which include homomorphic encryption and zero-knowledge proofs, facilitate privacy-enhancing analytics that reconcile customized financial offerings in opposition to people’s privacy protection. Regulatory technology embedding using compliance-by-design architectures in regulatory technology help automate policy application and reporting while advanced trust protocols using biometric authentication, behavior analysis, and machine learning algorithms prevent fraud while ensuring frictionless user experiences. The intersection of distributed architectures, privacy-retaining technologies, and inclusive design styles generates financial structures that cater to various populations even as adhering to demanding safety and regulatory compliance in diverse jurisdictions.

Chao Wang, Willy Susilo, Yudi Zhang, Yumei Li · 5 authors

Cloud computing facilitates scalable data sharing across multiple organizations and users, but also raises concerns about data privacy. Matchmaking encryption (ME) is a prominent technique that enforces bilateral access control in cloud services such as cloud marketplace, allowing both senders and receivers to specify policies for the encrypted data to be revealed. However, receivers may be at risk of being exposed to malicious or harmful content, thus undermining their trust in cloud service platforms. To this end, we introduce MBAC, a content-moderated bilateral access control framework for privacy-preserving cloud data sharing services, which allows receivers to acquire data from authentic senders while preserving their anonymity, and report malicious content in a verifiable manner, i.e., empowering the service provider to hold senders accountable. MBAC is built upon a novel primitive called franking broadcast ME (FBME), which generates a franking signature for the data by designating the service provider as the moderator to ensure accountability and deniability, and encrypts both the data and its franking signature while embedding the sender secret key for privacy and authenticity. We then present a concrete construction of FBME from key-private public key encryption, strongly unforgeable one time signature and non-interactive zero-knowledge proof. Formal security analysis and extensive experiments demonstrate that MBAC provides efficient bilateral access control and content moderation for cloud data sharing services.

Yang Xu, Qixin Wang, Yufei Ren, Ying Hu · 7 authors

ABSTRACT Transitive signatures are a special type of homomorphic signature proposed by Turing Award winners Micali and Rivest, which are highly suitable for authenticating dynamically growing graph‐based data systems. In such a signature scheme, anyone with the signer's public key is allowed to generate a signature for a composed edge , from two signatures on adjacent edges and . To prevent the problem of malicious dissemination of signatures by verifiers leading to data privacy leakage, researchers have proposed a series of universal designated verifier transitive signature (UDVTS) schemes. However, existing work requires that the designated verifier create its own secret‐public key pair using the public key parameters provided by the signer. Besides, these schemes suffer from significant performance defects due to expensive pairing or exponentiation operations. In this work, we design a pairing‐free and exponentiation‐free UDVTS proof scheme based on the SM2 digital signature algorithm and a zero‐knowledge proof scheme. We prove the security of our construction based on rigorous cryptographic assumptions. The performance comparison with related work shows that our UDVTS proof scheme has an optimal computational cost and desirable communication cost. For example, compared to the state‐of‐the‐art work, we reduce the signing cost by and the designated verification cost by .

Dennis Hamm, Erwin Kupris, Thomas Schreck

No abstract is available for this record.

Jitendra Sharma, Jigyasu Dubey

Transaction-Ordering Dependence (TOD) is a potential vulnerability of blockchain-based smart contracts, which allows malicious actors to exploit the order of transactions to obtain financial profit through front-running and back-running strategies.The purpose of this paper is to present ZkDelay, a new framework that jointly uses commitment schemes and Verifiable Delay Functions (VDFs) to counter TOD in decentralized applications.ZkDelay introduces a two-step transaction scheme: a user makes a cryptographic commitment to a transaction without announcing its purpose, and then, upon completing a verifiable delay with a VDF, the intended transaction can be revealed and carried out.This temporal discontinuity, combined with cryptographic acknowledgments, prevents adversaries from interfering with actionable knowledge in real-time, thereby eliminating any orderingbased attack possibilities.Moreover, ZkDelay is transparent and trustless, as it can be used to verify both commitments and delay execution through zero-knowledge proofs, without leaking sensitive data.Additional sections dedicated to rigorous security analysis and performance analysis in Ethereum-like environments are provided in the paper, demonstrating that ZkDelay incurs only a low amount of computational overhead and that it exponentially improves resistance to TOD attacks.The solution can be deployed in existing smart contract systems and adapted to DeFi protocols, order-sensitive auctions, and other mechanisms.ZkDelay addresses the challenge of integrating privacy-preserving mechanisms with the fairness of execution by providing a scalable and practical solution to one of the most prevalent security issues in smart contract environments.

Jianyu Zou, Songlin He, Xukang Lyu, Dongliang Chu

No abstract is available for this record.

Tiantian Wu, Yixuan Shen, Fan Zhang, You Jiang · 7 authors

No abstract is available for this record.

Zhexiu Tu, Charles Pizzuti

No abstract is available for this record.

Mohsen Ahmadvand, Pedro Souto

Zero-knowledge rollups rely on provers to generate multi-step state transition proofs under strict finality and availability constraints. These steps require expensive hardware (e.g., GPUs), and finality is reached only once all stages complete and results are posted on-chain. As rollups scale, staying economically viable becomes increasingly difficult due to rising throughput, fast finality demands, volatile gas prices, and dynamic resource needs. We base our study on Halo2-based proving systems and identify transactions per second (TPS), average gas usage, and finality time as key cost drivers. To address this, we propose a parametric cost model that captures rollup-specific constraints and ensures provers can keep up with incoming transaction load. We formulate this model as a constraint system and solve it using the Z3 SMT solver to find cost-optimal configurations. To validate our approach, we implement a simulator that detects lag and estimates operational costs. Our method shows a potential cost reduction of up to 70\%.

Yadiki Bhavashya Chandra

Digital payments have grown exponentially but face risks such as fraud, account takeover, and unauthorized transactions. This paper explores how blockchain technology, with its decentralized ledger, cryptographic integrity, and smart contracts, can secure digital payments and prevent fraud. We propose a permissioned blockchain architecture for payment systems, integrating identity management, escrow-based smart contracts, and audit-ready transaction logs. Illustrative simulations compare fraud-risk indices, transaction confirmation time, and per-transaction cost across traditional payment gateways and blockchain systems. The results indicate that blockchain can reduce fraud exposure while maintaining near real-time settlement. Challenges such as scalability, privacy, and regulatory compliance are also discussed. This study highlights blockchain?s potential as a preventive, secure mechanism for digital payments and sets the stage for future research integrating zero-knowledge proofs and federated learning.

Selvaragavan S, Karuppasamy L, Yuvan Sankar NKR, R Sylaja Vallee Narayan S. · 6 authors

The Internet of Things (IoT) has significantly transformed critical domain such has healthcare, transportation, industry, and smart cities by connecting billions of devices that can gather and share data. However, as it grows rapidly, IoT systems are grappling with significant issues around data security, user privacy, and trust. Centralized systems are particularly at risk due to single points of failure, latency issues, and potential privacy breaches, which makes them less ideal for large-scale use. While blockchain technology provides a way to decentralize and resist tampering, traditional frameworks can be too resource-heavy and impractical for low-power IoT devices.This paper introduces a Lightweight Blockchain Framework designed for Secure and Privacy-Preserving IoT Systems. It combines energy-efficient consensus mechanisms, trust-based reputation management, and a hybrid approach to on-chain and off-chain storage. By utilizing edge-assisted processing, we can cut down on computational demands, and cryptographic techniques like elliptic curve cryptography and zero-knowledge proofs help maintain privacy during authentication. The framework is built with scalability, low latency, and effective communication in mind, making it suitable for real-time IoT applications.Simulation results show that our proposed framework can reduce communication costs by 35%, lower latency, and improve privacy protection compared to traditional blockchain-based IoT systems. These findings indicate that the framework is not only lightweight and secure but also practical for applications that need privacy and quick responses, such as in healthcare, industrial automation, and smart infrastructure.

Mohamed Abdessamed Rezazi, Mouhamed Amine Bouchiha, A. Bendada, Yacine Ghamri-Doudane

Roaming settlement in 5G and beyond networks demands secure, efficient, and trustworthy mechanisms for billing reconciliation between mobile operators. While blockchain promises decentralization and auditability, existing solutions suffer from critical limitations-namely, data privacy risks, assumptions of mutual trust, and scalability bottlenecks. To address these challenges, we present B5GRoam, a novel on-chain and zero-trust framework for secure, privacy-preserving, and scalable roaming settlements. B5GRoam introduces a cryptographically verifiable call detail record (CDR) submission protocol, enabling smart contracts to authenticate usage claims without exposing sensitive data. To preserve privacy, we integrate non-interactive zero-knowledge proofs (zkSNARKs) that allow on-chain verification of roaming activity without revealing user or network details. To meet the high-throughput demands of 5G environments, B5GRoam leverages Layer 2 zk-Rollups, significantly reducing gas costs while maintaining the security guarantees of Layer 1. Experimental results demonstrate a throughput of over 7,200 tx/s with strong privacy and substantial cost savings. By eliminating intermediaries and enhancing verifiability, B5GRoam offers a practical and secure foundation for decentralized roaming in future mobile networks.

G Anvith, Nithish Kushal Reddy, Ragini Tripathi, C. R. Kavitha

Federated learning enables multiple clients to collaboratively train a shared model without exchanging raw data, but it raises privacy and integrity concerns when model updates traverse untrusted channels. In this project, we develop a secure federated learning pipeline that combines the CKKS homomorphic-encryption scheme with Groth-16 zero-knowledge proofs to protect client updates during transmission and to verify that each update stays within an agreed-upon norm bound. We benchmark CKKS parameters (poly_modulus degree, coefficient_moduli, and scale) on real-world model vectors to identify an optimal setting—8192-degree with two primes (60-bit and 40-bit) at a 232scale—that offers sub-100 ms encryption, minimal error, and moderate ciphertext sizes ( 3.3 MB). Clients train a small convolutional network on disjoint partitions of the MSTAR SAR dataset, generate succinct ZK proofs for each 128-element weight chunk, encrypt those chunks under the selected CKKS context, and submit both ciphertexts and proofs to dedicated servers. The homomorphic-aggregation server sums encrypted updates, while the ZKP server enforces correctness by rejecting any proof that violates the norm constraint—demonstrated by catching an intentionally malicious client. End-to-end testing confirms that the combined CKKS+ZKP pipeline preserves model accuracy and ensures both confidentiality and integrity of federated updates.

Zilong Han, Chaoqun Kang, Wanqiao Wang, Yuling Li

Aiming at the problems of single-point failure, privacy leakage, and high communication delay existing in the process of massive intelligent terminals accessing the new power system with traditional centralized identity authentication methods, this paper proposes an efficient identity authentication method for power terminals based on blockchain and non-interactive zero-knowledge proof. By improving Schnorr protocol, a dynamic random number driven non interactive authentication mechanism is designed to avoid the high delay of private key transmission and multi round communication. At the same time, in combination with the distributed ledger characteristics of the blockchain, the terminal public key and authentication records are decentralized stored, eliminating the dependence on a single CA. In this paper, we propose an aggregate signature method, which aggregates the zero knowledge proofs of multiple devices into a total signature, reducing the computation and communication overhead when authenticating a large number of terminal devices. This paper analyzes the performance of the proposed method using building simulation blockchain on the Hyperledger Fabric platform. Compared with other methods, this method performs well in the actual authentication phase, and reduces the time cost by more than 4.5%. Through batch certification test, compared with single terminal certification, the time cost is reduced by more than 80%. The security analysis results show that this method can resist replay attacks, phishing attacks, etc., and ensure the identity anonymity of terminal devices, the confidentiality of private keys, and the integrity of data transmission.

Jiayu Tian

To address the absence of process-level verifiability in federated learning, a verifiable architecture, zero-knowledge proof-verified and blockchain-audited federated learning (zk-BcFed), is proposed by integrating zero-knowledge proofs with blockchain. For each local model update, a multi-constraint zero-knowledge proof is generated by the client, and verified cryptographic evidence is recorded on-chain, enabling formal verification of local training without disclosure of private data. Across benchmark datasets including SVHN, FashionMNIST, and CIFAR10, among others, enabling zero-knowledge proofs is observed to produce a negligible change in accuracy while substantially improving robustness under model poisoning attacks. Collectively, zk-BcFed safeguards the computational integrity and correctness of federated learning and provides a reliable verifiability mechanism with modest overhead.

ALTAMURA, NICOLA

Marine carbon dioxide removal (mCDR) projects are increasingly recognized as a strategic pillar in climate change mitigation. However, their effectiveness and credibility critically depend on the ability to implement secure and verifiable Monitoring, Reporting, and Verification (MRV) procedures, particularly in remote, adversarial, and resource-constrained environments like underwater ecosystems. Despite growing interest in mCDR, current MRV frameworks remain inadequate for such challenging contexts, lacking essential mechanisms to ensure secure device identity, reliable data provenance, and verifiable auditability, as mandated by standards such as ISO 14064-2 and ISO 14064-3. This thesis addresses these limitations by first focusing on the fundamental security challenges that arise in underwater untrusted environments. To overcome these barriers, it introduces a set of modular, composable building blocks that integrate: i) decentralized identifiers (DIDs) for self-sovereign identity management, ii) physically unclonable functions (PUFs) to cryptographically bind secrets to hardwaredevices, iii) non-interactive zero-knowledge proofs (NIZKPs) to enable lightweight and privacy-preserving authentication, and iv) distributed ledger technologies (DLTs) to ensure immutable and verifiable data anchoring. These solutions are designed to be modular, interoperable, and composable, providing the necessary foundation to build secure, transparent, and standards-compliant MRV frameworks suitable for deployment in underwater and blue carbon ecosystems. Crucially, the proposed work does not merely adapt to MRV requirements but proactively resolves critical security gaps that existing MRV models overlook, thus enabling trustworthy data collection, secure identity management, and verifiable certification in these complex environments. The individual building blocks have been implemented and validated on constrained embedded platforms (e.g., ESP32-C3), demonstrating their feasibility under the constraints of underwater sensing. Formal security guarantees are established using symbolic analysis, while empirical evaluations quantify the computational and communication overhead of each component under realistic conditions. By starting from low-level cryptographic primitives and addressing core security challenges, this thesis delivers a foundational contribution to the development of secure, verifiable, and scalable MRV infrastructures for underwater and blue carbon applications. The proposed approach supports the creation of trustworthy, standards-aligned MRV frameworks, enabling verifiable environmental accountability even in hostile and resource-constrained deployment scenarios.

Princi, Pratyush Pratyush, Chakridhar Reddy Lokireddy, Renu Mishra

There has been a major cause of concern, especially concerning the integrity of political financing, since the idea of existing electoral bonds lacks transparency, traceability, and accountability. Although these systems are meant to help formalize political contributions, they usually help to allow anonymous contributions and disclosure at will, violating both the trust of a population and democratic responsibilities. The paper entails the design, development, and flow of ElectraChain, a decentralized system and blockchain-based electoral bond management platform. With the help of smart contracts and privacy-preserving cryptographic schemes like Zero-Knowledge Proofs (ZKPs), ElectraChain will preserve the tamper-proofness of bond transactions recording and preserve donor anonymity. The architecture proposed has secure KYC processes included, automated compliance procedures, and publicly available dashboards to go to the next level of transparency without any breach of privacy. Blockchain technology has been a device that allows the development of a viable prototype that supports the feasibility, security, and scalability of the system. The paper also assesses the technical parameters of the system and comments on the possibilities of the use of such a system nationally, which becomes a major step towards transparent, accountable, and technology-driven political financing in democracies.

Ammad Aslam, Octavian Postolache, Sancho Oliveira

Wireless Sensor Networks (WSNs) are widely used in various applications that require secure and efficient data aggregation. A novel secure data aggregation scheme is proposed in this study. This is a novel combination of Blockchain technology and Zero-Knowledge Proofs (ZKPs). This scheme is broken down into three parts. The first is Blockchain based Data Aggregation for storage of tamper proof and immutable data. Second, anonymity with ZKP to provide privacy preservation and secure identity verification. Finally, we combined Blockchain and ZKP operation to achieve energy efficiency and robust security. The proposed scheme guarantees data confidentiality, integrity, anonymity, and resilience to malicious attacks, and at the same time handles the energy optimization problem in WSNs. We implemented the proposed scheme in MATLAB to evaluate its performance with metrics like stability, jitter, latency, and throughput. Simulation results show that the proposed scheme achieved robust security in terms of data integrity and confidentiality by slightly compromising on the availability aspect.

Мордвінов, Р.І.

The article presents a comprehensive overview of zero-knowledge proof (ZKP) protocols as a fundamental concept of modern cryptography. The historical background of their emergence and the main properties ensuring reliability and confidentiality, i.e., completeness, soundness, and zero-knowledge — are considered. A classification of protocols into interactive and non-interactive ones is provided, with a special focus on modern solutions such as the zk-SNARK and the zk-STARK. The mathematical foundations of ZKPs are described in detail, including discrete logarithm proofs, the use of homomorphic encryption, polynomial commitments, hashing, and elliptic curves. Practical application areas are analyzed, including cryptocurrencies (Zcash, Ethereum), authentication systems, digital identity, and electronic voting. The advantages of using ZKPs are shown, such as enhanced privacy, reduced need for trusted intermediaries, and strengthened security. At the same time, key challenges are outlined, including scalability, implementation complexity, the problem of trusted setup, and potential vulnerability to quantum computing. It is concluded that zero-knowledge proof protocols are a powerful tool for ensuring confidentiality and reliability of digital systems, while further research is aimed at creating more efficient and quantum-resistant solutions.

Qingzhen Meng

Predictive maintenance in cross-border unmanned logistics systems (CBULS) faces persistent challenges, including data privacy, system heterogeneity, and collaborative efficiency. Existing studies that combine federated learning with blockchain address only partial aspects—such as communication or trust—but fail to effectively handle non-independent and identically distributed (non-IID) data, integrate multi-layer privacy, or design consensus mechanisms tailored to cross-border logistics. This paper proposes a predictive maintenance framework that integrates an improved FedProx algorithm with a hybrid Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) consensus. The framework incorporates zero-knowledge proofs, fully homomorphic encryption, and local differential privacy, while employing hierarchical architecture and sharding for scalability. Simulation results show that the proposed method improves prediction accuracy by 6.9% compared with FedAvg and 3.7% compared with FedProx, enhances privacy protection by over 12%, increases system throughput by approximately 23%, and reduces transaction confirmation latency by nearly 18%. These results demonstrate that the framework provides a secure, efficient, and scalable solution for predictive maintenance in CBULS.