With the advancement of the information age, the widespread application of electronic evidence in fields such as justice and finance has brought new challenges. Although existing blockchain electronic evidence sharing schemes have immutability and transparency, they still have shortcomings in access control, data privacy protection, and efficiency. In addition, traditional attribute encryption strategies lack effective revocation mechanisms and cannot fully protect privacy when implementing fine-grained access control. Therefore, in order to address the above limitations, a blockchain electronic evidence sharing scheme based on an improved ciphertext policy attribute encryption combined with zero knowledge proof technology has been proposed. The research innovatively introduces revocable ciphertext strategy encryption, which addresses the security risks caused by decryption key leakage through revocation function, ensuring the secure storage and sharing of electronic evidence. Meanwhile, the study also improved the PBFT consensus algorithm to enhance its performance in handling large volumes of transactions. The results showed that the storage TPS of the research model reached 492, and the query TPS reached 655. The computational cost of improving the PBFT consensus algorithm is 1.94 × 10 4 , and the maximum computational cost of the electronic evidence access control model based on zero knowledge proof is 509. Compared with traditional blockchain based electronic evidence sharing methods, the improved method not only enhances storage and sharing efficiency, but also further strengthens privacy protection capabilities by combining zero knowledge proof technology. In summary, the research method effectively achieves secure sharing and privacy protection of electronic evidence on blockchain, providing support and reference for electronic evidence storage in fields such as justice and finance. However, there are still challenges in terms of scalability and data storage in the research, so algorithms can be optimized in the future to further improve the application scope of the system.
L. Bharathi, Kabita Thaoroijam, Sri Raman Kothuri, P Joel Josephson · 6 authors
The blistering development of the Industrial Internet of Things (IIoT) has brought serious issues to the maintenance of large-scale sensor data security and processing with low latency and scalability. Conventional central and edge-only solutions are either limited in the number of trust bottlenecks or restricted in the detection accuracy, thus a hybrid solution is required. This study establishes a Blockchain-AI composite model, where federated anomaly detection and a superior Delegated Proof-of-Stake (eDPoS) consensus mechanism system are used to efficiently and safely process big data on IIoT scenarios. This methodology gives the analytical models that are vital in throughput, latency and the likelihood of hostile takeover. Researcher experimented the Indian IIoT and Blockchain Synthetic Dataset which includes DPoS information under a wide range of conditions, including safe and malicious adversarial stake attacks. It was found to significantly (up to 20 percent) improve throughput over vanilla DPoS, but latency is minimized under medium-delay networks and can anomaly detect (AUC [?] 0.93) with errors nearly equal to centralized (under 5 percent) baselines. Security analysis provides resistance to stake-boost attacks and optimization of storage using lightweight anchoring. This paper makes the framework a scalable and secure IIoT deployment solution, between blockchain consensus and AI-driven anomaly detection.
Because of the rapid acceleration of cloud computing, data transfer security and intrusion detection in cloud networks have become emerging areas of concern. All traditional security mechanisms have central vulnerabilities, cannot detect real-time threats, and are ineffective against zero-day attacks. Signature-based approaches of existing intrusion detection systems (IDS) do not cover the dynamically changing nature of cyber threats. Conventional blockchain security methods suffer from poor scalability and dynamic threat analysis. Therefore, this research proposes integrating Ethereum Blockchain and Deep Learning to construct a well-founded security framework for cloud networks with data migration security and real-time intrusion detection. The architecture has five distinct methods, each of which deals with particular security issues. Blockchain-Aware Federated Learning for Secure Model Training (BAFL SMT) guarantees tamper-proof and decentralized deep learning model training, which reduces model poisoning attacks by 98.4%. Graph Neural Networks for Adaptive Intrusion Detection (GNN-AID) captures graph structures for real-time anomaly detection in networks while reducing false positives to 1.2%. Quantum-inspired Variational Autoencoders (QI VAE ZDAD) provide enhanced zero-day attack detection, with an improved detection rate of 92%. Self-Supervised Contrastive Learning for Blockchain Security Auditing (SSCL-BSA) detects smart contract vulnerabilities automatically, resulting in an 87% reduction in fraud risk. Finally, Hierarchical Transformers for Secure Data Migration (HT SDM) enhance the transfer security of large-scale cloud data, achieving an attack classification accuracy of 99.1%. Overall, this multi-layer security framework will greatly enhance cloud security by preserving data integrity, cutting down the intrusion detection time by up to 65%, and enhancing response mechanisms. By marrying the immutable transparency of blockchain with superior anomaly detection at deep learning, this research provides a scalable, real-time, and intelligent approach to strengthening security against the backed-up transfer of data within cloud networks.
Electronic Health Records (EHR) is the main core of modern healthcare, but interoperability across different blockchain platforms is a key challenge. This work proposes a cross-chain middleware architecture, which facilitates secure and real-time synchronization of EHR data between Hyperledger Fabric (private blockchain) and Ethereum Sepolia Testnet (public blockchain). The framework integrates AES-256 encryption and Inter Planetary File System (IPFS) as decentralized storage to enhance patient privacy. To facilitate interoperability across the blockchains the research introduces a smart middleware layer. This layer autonomously monitors the blockchain events, processes encrypted CIDs, enforces real time cross chain consistency and smart contract-based access control. The experimental evaluation shows that proposed framework achieves low synchronization times (< 195 ms), low gas and latency costs, small encryption overhead (< 4–5 KB), robust file storage and retrieval through IPFS. Such positive evaluations with scalable and real-time deployment, sets the foundation of patient centric interoperable healthcare ecosystems.
A systemic "evidentiary deficit" now characterizes automated global civicsystems, undermining regulatory oversight, institutional accountability, and public trust in high-stakes domains. The increasing opacity of high-speed, algorithmically-driven decisions in finance, public health, and environmental governance creates un-auditable risks. This report posits Ternary Logic (TL) as a neutral, non-ideological infrastructure framework designed to remediate this deficit. TL extends traditional binary logic by introducing a formal, third logical state: 0 (Epistemic Hold), distinct from 1 (Proceed) and -1 (Halt). This 0 state functions as a mandatory, auditable "computational hesitation" triggered by predefined uncertainty or risk thresholds. By instrumenting this pause, TL transforms deliberation and uncertainty from an operational failure into a cryptographically verifiable evidentiary asset. This report details the TL architecture through its Eight Pillars, which provide an integrated "accountability stack" mapping institutional policy to cryptographic proof. It describes the tri-cameral governance model—Technical Council, Stewardship Custodians, and Smart Contract Safeguard—architected for long-term resilience and prevention of institutional capture. Furthermore, it details the technical architecture, including a dual-lane, low-latency (<300ms) design, a hybrid-shield (public/private) ledger system, and a novel cryptographic stack (combining Ephemeral Key Rotation, Zero-Knowledge Proofs, and Cryptographic Erasure) that simultaneously satisfies regulatory demands for auditability, legal requirements for privacy (e.g., GDPR), and commercial protection of trade secrets. This framework provides a sovereign-grade blueprint for establishing provable accountability in systems governed by institutions such as the Bank for International Settlements (BIS), U.S. Securities and Exchange Commission (SEC), U.S. Food and Drug Administration (FDA), and World Health Organization (WHO).
As global energy systems pivot toward net-zero, Carbon Capture, Utilization and Storage (CCUS) has emerged as a foundational tool to mitigate industrial emissions (Abu Zahra et al. 2007; Boot-Handford et al. 2014; IEA 2020). However, the effectiveness, financeability and scalability of CCUS projects are severely hindered by traditional Monitoring, Reporting and Verification (MRV) workflows (IEAGHG 2017; U.S. DOE 2018–2023; Verra 2023). These legacy processes are typified by siloed data collection, infrequent audits, manual record reconciliation and a lack of trust between stakeholders. This results in substantial verification delays, compliance risks and a sluggish, opaque carbon market where the link between physical decarbonization and financial value is weak (ISO 2018; Dutta et al. 2021; Hartmann et al. 2023). Although real-time IoT sensing, AI-powered analytics and blockchain-based recordkeeping have each been explored independently (Garcia Freites and Jones 2021; Wang et al. 2019; Hartmann et al. 2023), no prior framework has fused these into a seamless, robust and self-governing digital pipeline for CCUS MRV and carbon-credit management at industrial scale (Hütten et al. 2022). This paper introduces a novel architecture that: Deploys dense, high-fidelity IoT sensor networks and edge-compute nodes across the full CCUS value chain (Abu Zahra et al. 2007; IEAGHG 2017; Dutta et al. 2021);Leverages advanced, multi-tiered AI/ML for operational intelligence, predictive reliability and financial optimization (Breiman 2001; Hochreiter and Schmidhuber 1997; Chen et al. 2019; Bender et al. 2021);Employs a hybrid off-chain/on-chain data model for performance, privacy and regulatory-grade auditability (Garcia Freites and Jones 2021; Boneh and Shoup 2020; NIST 2024);Harnesses Layer-2 blockchain with decentralized oracles and smart contracts to automate MRV validation, progressive credit minting, escrow and transparent marketplace integration (Buterin et al. 2014; zkSync 2024; Bai et al. 2021; KlimaDAO 2024).
John Adeyemi O, Folasade Yetunde Ayankoya, Kuyoro S. O
The advancement of technology has positioned blockchain and machine learning (ML) as transformative forces in finance. Blockchain’s decentralized structure ensures secure and transparent transactions, while ML processes vast data to identify patterns and enhance decision-making. Their integration offers significant potential for fraud detection, risk assessment, and transaction optimization. Blockchain provides a tamper-proof environment, ensuring data integrity and reducing fraud. Meanwhile, ML detects anomalies, predicts market trends, and automates processes, improving financial security and efficiency. However, challenges such as scalability, computational demands, and data privacy hinder widespread adoption. Blockchain struggles with high costs and limited throughput, while ML requires significant resources and quality data. Emerging solutions like federated learning for privacy-preserving ML, zero-knowledge proofs for secure transactions, and hybrid blockchain models for scalability aim to address these challenges. Overcoming these barriers will enable a more secure, efficient, and data-driven financial ecosystem.
Ethereum ist seit Jahren die größte Smart-Contract-Blockchain und nach Bitcoin die zweitgrößte Blockchain-Plattform. Smart-Contracts, die als dezentrale Anwendungen beschrieben werden können, laufen auf einer gemeinsamen Rechenplattform, auf der alle Teilnehmer auf einer geteilten Codebasis arbeiten. Zur Absicherung ist es nötig, dass ein Konsens über die Ein- und Ausgaben aller Smart-Contracts geschaffen wird. Die Ausführung von Smart-Contract-Code verbraucht sogenannte Gas-Einheiten, die als eine Art Treibstoff betrachtet werden können. Gas-Einheiten zeigen den erforderlichen Rechenaufwand an und haben direkte Auswirkungen auf den realen Energieverbrauch. Daher sollten idealerweise alle Smart-Contracts so implementiert sein, dass sie möglichst wenig Gas-Einheiten verbrauchen. Derartige Codeoptimierungsansätze sind nicht trivial. Zum Zeitpunkt des Verfassens dieser Diplomarbeit gibt es bereits solche Mechanismen, welche teilweise direkt in den gängigen Compilern integriert sind. Solche Mechanismen basieren in der Regel auf festen Mustern, welche manuell beschrieben werden müssen und dann auf Smart-Contracts angewendet werden können. In dieser Arbeit haben wir untersucht, ob klassische Verfahren zur Erkennung von Codeähnlichkeiten verwendet werden können, um Optimierungsmuster automatisch aus Quellcode-Repositories ableiten zu können. Zunächst haben wir einen Symbolic-Execution-Ansatz untersucht, welcher sich aufgrund von technischen Einschränkungen und der Abhängigkeit von veralteten Compiler-Versionen als ungeeignet erwies. Daraufhin haben wir einen Fingerprinting-Ansatz basierend auf Kontrollflussgraph-Blöcken gewählt. Mithilfe von Slither konnten wir Metriken wie Cyclomatic-Complexity, Fan-Out und Informationsfluss-Metriken extrahieren und anschließend Distanzen zwischen Codestücken berechnen, um mit den Ergebnissen potenzielle semantische Code-Klone zu erkennen. Wir haben die Evaluierung unseres Ansatzes auf 1.200 manuell markierten Smart-Contracts aus einem Datensatz mit 160.000 Einträgen durchgeführt, was zu 574 Vergleichen führte und konnten eine korrigierte Genauigkeit von 88% für die Erkennung von semantischen Code-Äquivalenzen auf Blockebene erzielen. Für 1.300 Code-Paare haben wir zusätzlich eine Gasverbrauchsmessung durchgeführt, indem wir die Blöcke in generierte Smart-Contracts verpackt und auf einer lokalen Blockchain ausgeführt haben. Dabei konnten wir tatsächliche gasreduzierende Codeänderungen identifizieren. Trotz einiger wesentlichen Einschränkungen zeigt das, dass das Mining gasoptimiertem Codes aus versionierten Source-Code-Repositories mittels Code-Metriken möglich ist.
Esther Uzoka, Bisola Akeju, Olumide Kumuyi, David Excel Ozowara
The Framework for Data Governance and Compliance Across Distributed Multicloud Infrastructures provides a comprehensive model for managing data integrity, privacy, and regulatory alignment in increasingly complex hybrid and multicloud environments. As organizations adopt distributed computing to enhance scalability, resilience, and performance, they face significant challenges in maintaining consistent governance across heterogeneous platforms operated by multiple providers. This framework establishes a unified governance architecture that integrates policy-based orchestration, automated compliance auditing, and federated identity management to ensure data sovereignty, accountability, and interoperability across diverse cloud ecosystems.At its core, the framework emphasizes data classification, lifecycle management, and access control standardization. Sensitive data are categorized by regulatory requirement and security level, while dynamic policies enforce encryption, anonymization, and retention protocols in accordance with frameworks such as GDPR, HIPAA, and ISO 27001. By leveraging federated metadata catalogs and distributed ledgers, the system enables traceable data provenance and immutable audit trails across hybrid environments. A zero-trust security paradigm further ensures that all access requests are continuously verified, regardless of origin, thereby mitigating insider threats and cross-cloud vulnerabilities.The framework also integrates AI-driven compliance monitoring to detect policy violations, automate reporting, and support adaptive governance in real time. Through interoperable APIs and compliance-as-code implementations, organizations can harmonize data policies across public, private, and edge cloud resources while maintaining jurisdictional and contractual adherence.In promoting transparency and resilience, this framework underscores the importance of cross-sector collaboration among regulators, cloud providers, and enterprises. By unifying governance, security, and compliance strategies, it advances a scalable model for secure data management in distributed infrastructuresenabling innovation, regulatory trust, and sustainable digital transformation in the multicloud era.
Prajakta Sudhir Khade, Aarushi Santosh Gode, Rajeshkumar U. Sambhe
The exponential rise of cyber threats has revealed the vulnerabilities of centralized security systems, including susceptibility to insider attacks, single points of failure, and regulatory inefficiencies. This paper investigates blockchain as a transformative backbone for cybersecurity, focusing on its potential to ensure data integrity, decentralize trust, and mitigate advanced cyber risks. Beginning with a comprehensive literature review, the study examines the fundamentals of blockchain technology—distributed ledgers, consensus mechanisms, and cryptographic primitives—that enable tamper-proof, transparent, and secure digital ecosystems. The challenges of centralized systems are contrasted with blockchain’s resilience, highlighting its role in eliminating bottlenecks and enhancing trust. Applications across identity management, IoT security, supply chains, and e-governance are analyzed alongside a proposed methodology that integrates blockchain with artificial intelligence, IoT, and quantum-resilient models. Real-world case studies demonstrate blockchain’s adoption in healthcare, government, and industrial systems, while challenges such as scalability, interoperability, and compliance are critically assessed. Collectively, this study underscores blockchain’s pivotal role in shaping next-generation cybersecurity architectures.
Saurabh V. Magdum, Sonali Patil, Deepali Nilesh Naik
Federated Learning (FL) revolutionized the field preserving machine learning by facilitating collaborative model training among decentralized clients in absence of raw data. The classic architectures of FT, in contrast, usually rely on a centralized aggregator, which poses threats such as single points of failure, data poisoning, and model inversion attacks. Use of combination of Blockchain technology holds the promise solution via replacement of centralized aggregators with decentralized consensus mechanisms, improving trust, transparency, and data integrity. The present bibliometric analysis considers the correlation of Blockchain and Federated Learning (BFL), with special reference on flagship aggregation algorithms like FedAvg, FedProx, and FedBN, specifically the blockchain networks such as Ethereum, Hyper- ledger Fabric, and Polkadot. Additionally, the paper records actual- world use cases in privacy-sensitive applications like healthcare, finance, and IoT, using benchmark datasets such as MIMIC-III, NASDAQ stock data, and EdgeIIoTset. The proposed study identifies Key trends, timeless findings, and future directions In BFL, gaining perceptual insights of its growing significance for building trustworthy, privacypreserving AI systems.
Widespread adoption of Blockchain Technology has emphasized smart contracts as vital units of digital transactions. In the brief history of smart contracts, significant losses have occurred owing to unexplained vulnerabilities in blockchain-loaded contracts. Smart contract vulnerabilities endanger blockchain technology's viability and confidence. Blockchain-based applications rely on smart contracts to automate and trustlessly execute agreements. Their immutable and self-executing nature leaves them vulnerable to security vulnerabilities, which have caused major financial losses in decentralized platforms. Traditional static and symbolic analysis tools often miss sophisticated or obfuscated vulnerabilities, resulting in insufficient coverage or excessive false positives. These restrictions led the investigation of Deep Learning (DL) algorithms for detecting vulnerabilities in smart contract crucial since they can learn complicated patterns from code representations without manual feature engineering. Deep learning based methods for detecting vulnerabilities in smart contracts are thoroughly evaluated. We rigorously assess state-of-the-art DL models including BiLSTM, BiGRU, CNNLSTM, GCN, and CodeBERT using publicly available dataset. We provide a taxonomy of DL-based detection techniques and standardize performance criteria including accuracy, F1-score, and detection latency. Experimental results show model architecture trade-offs in detection, computational efficiency, and generalization.
Blockchain technology offers incredible value and opportunity in delivering secured, decentralized transactions but faces significant challenges as it relates to scalability, which hinders adoption and use. As in the maturation of blockchain technology, challenges related to its scalability are important for latest technologies as they can affect scalability and efficiency. These types of challenges to scalability generally emerge through increased user and transaction volumes and cause losses in performance and efficiency as they pertain to a congested network, storage of data, and speed in processing time. Additionally, traditional consensus mechanisms, like proof of work, seamlessly create challenges as they pertain to amounts of computational power, and other resources allow allocation associated with work completion. As we can imagine, potential solutions have already emerged that help to mitigate overcoming these challenges. Solutions related to Layer 2 scaling, such as side chains or payment channels, add an alternative layer to the transaction applications and, theoretically, increase throughput and financially through decreased network congestion. Sharding, which splits the blockchain into smaller, more manageable segments, also improves operational efficiency. Moreover, advancements in consensus algorithms, including Proof of Stake and hybrid models, aim to boost scalability while optimizing resource use. This research explores the primary scalability challenges faced by blockchain systems. It reviews the cutting-edge solutions being developed to improve their performance, ensuring that blockchain technology can effectively support the increasing demands of a decentralized digital world.
Blockchain is known for being a decentralized ledger with distributed storage. It has changed whole industries across borders by increasing security, transparency, and reliance on intermediaries. Thus, from its initial design for cryptocurrencies like Bitcoin, Blockchain extends its transformative potential for a wide range of fields such as finance, supply chain management, and healthcare. The contribution of this research is an in-depth analysis of blocks, transactions, and consensus mechanisms constituting the anatomy of a blockchain. We have paid particular attention to block-structure research in our work, emphasizing that a block is an indivisible information unit, each containing transactional data and cryptographic hashes that link it to the previous block. One of the most valuable parts of our research consists of an innovative analysis of a consensus mechanism. We explain how different algorithms ensure the validity and sequence of transactions among the network nodes, and review the strengths and weaknesses of algorithms, namely, Proof of Work (PoW), Proof of Stake (PoS), and Delegated Proof of Stake (DPoS). The key highlights in this work are the case studies of well-established blockchain platforms, including Bitcoin and Ethereum. These manifest our insight into their operational efficiencies and mechanisms for security. Further, we demonstrate empirical results on the processing times for transactions and scalabilities of blockchains under different network conditions. Additionally, the challenges of scalability and energy consumption are put forth, for which novel approaches may be proposed for future blockchain development. The study contributes to the further development of blockchain technology by informing future research directions toward solving the existing limitations and exploring new applications within emergent sectors.
Oct 29, 2025·2025 International Conference on Electrical, Electronics, and Computer Science with Advance Power Technologies - A Future Trends (ICE2CPT)
Since the introduction of Bitcoin in 2008, the blockchain technology as its underlying architecture, has attracted attention from various sides due to its decentralized and distributed computing characteristics. AS the core advantage of blockchain technology, the consensus mechanism determines various characteristics of blockchain, such as security, scalability, and decentralization. Currently, there are many consensus mechanisms suitable for different scenarios. This paper studies the existing consensus mechanisms from the perspectives of algorithm principles, performance, etc. Firstly, this article divides the existing consensus mechanisms into Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT). Secondly, for each type of consensus mechanisms, the study analyzes their algorithmic principles, understands typical solutions and latest ones, clarifies the advantages, disadvantages, and the possible attack methods of various consensus mechanisms. Finally, the paper defines the basic requirements for new consensus mechanisms. It aims to help break through the application bottlenecks of blockchain technology and promote the development of blockchain technology in various scenarios.
Blockchain is rapidly developing in many sectors, and it is promising its application specifically for healthcare. This chapter examines the potential of blockchain technology to offer solutions to some of the key problems in healthcare systems, including data breaches, fragmented systems, and the apparent lack of patient control over their health records, all of which could be addressed through the use of secure and transparent blockchain systems. With its sophisticated encryption technology, consensus algorithms, and decentralised access control, Blockchain is well-suited to securing sensitive healthcare data. The chapter explores the technical architecture of Blockchain in healthcare, contrasting public and private blockchains and looking at the role of consensus mechanisms such as proof-of-stake and proof-ofauthority. Some critical healthcare-based applications are the management of secure patient data, the management of the drug supply chain, clinical trials, and medical billing, in which Blockchain helps keep the data integrity, fraud prevention, and compliance assurance. Despite its promising benefits, challenges such as scalability, high implementation costs, and resistance to change within the healthcare ecosystem are discussed as well. By addressing these limitations, Blockchain can significantly improve the security and transparency of healthcare systems.
Distributed Ledger Technology (DLT) has emerged as a promising solution for constructing decentralized and trustworthy platforms in recent years. Although blockchain, as a form of distributed ledger technology known for its tamper resistance and trustworthiness, seems to be an ideal solution for addressing trust issues, common blockchain systems face limitations due to the ever-growing volume of data. In this paper, we propose a dynamic storage scheme calledEfficiency-awareLow-storageScalability (ELS), which allocates variable data storage for the distributed ledgers. Our approach is designed to provide load balancing and security, allowing dynamic nodes to join or exit the network seamlessly. We utilize graph theory to group storage nodes in the blockchain by determining their network distances. These groups are then mapped into points in a two-dimensional virtual plane, meaning that block storage depends on the coordinates within the virtual plane. To balance storage performance with the evolving block allocation strategy, we develop a Voronoi diagram-based method to achieve near real-time adoptions that meet dynamic allocation requirements in blockchain systems. Experimental evaluations have demonstrated that our approach can reduce storage requirements by approximately 93% at each node, compared to full replication in 10000 block chains.