Jyoti Neeli, Vijaya Shetty S, N. N. Srivastava, K. Venkatesh Sharma · 5 authors
The Solana blockchain serves as the foundation for the Finance (DeFi) Lending Protocol. The proposed protocol seeks to address the high fees and sluggish transaction speeds of current Ethereum-based protocols by utilizing Solana's high throughput and low-latency infrastructure. Among our contributions are a real-time interest rate model, improved liquidation logic, and a novel lending pool mechanism. According to experimental results, fees are lowered by more than 90% and transaction speeds are increased by 85% when compared to Ethereum- based systems. According to the results, DeFi protocols based on Solana are ideally suited for extensive financial applications.
This paper discusses the obstacles to the capitalization of data elements, such as the difficulties in confirming data ownership, trust deficit, privacy breaches, and inefficiency of transactions, through a distributed solution based on blockchain technology. First, a data ownership confirmation mechanism based on a consortium blockchain is established by using the Merkle tree and PBFT (Practical Byzantine Fault Tolerance) consensus algorithm to achieve transparency and traceability of data ownership. Second, a multi-dimensional data value evaluation and RF-BP (Random Forest-Back Propagation) dynamic pricing mechanism are established by using machine learning algorithms to evaluate the value of data assets in a scientific manner. Third, a smart contract is established for pricing and payment, in order to achieve transaction automation and clearing and settlement. Finally, ZKP (Zero-Knowledge Proof) technology is applied to develop a mechanism for verifying compliance and privacy of data under the proposition of public review and "visible, invisible". Experimental results show that the proposed method reduces the average leakage risk and defense success rate under various attacks to 8.57 % and 97.1%, respectively. In terms of transaction efficiency, the proposed method achieves a throughput of 1250 TPS (Transactions Per Second) with a latency of 120 milliseconds at a 50-node scale. Overall performance is demonstrated with a confirmation and transaction success rate of 99.2% and 97.8%, respectively. The suggested framework provides reliable confirmation of data elements, scientific pricing, efficient trading and transaction processes, and privacy protection. It can support the vision of developing a secure, transparent and efficient market for data element circulation with technical feasibility and performance.
Modern cryptographic tools such as multi-party computation (MPC) and zero-knowledge proofs (ZKPs) offer strong, provable security guarantees—but these generic protocols remain impractical for production-scale machine learning (ML), especially in the era of large language models (LLMs). This thesis proposal advances the central claim that cryptographic protocols co-designed with the structure of specific ML subtasks can achieve practical efficiency without compromising privacy or verifiability. To validate this vision, this proposal develops three interconnected research thrusts: (1) Confidential Outsourced Training. Customized MPC protocols shift expensive cryptographic steps to local computations, enabling secure training of large models in untrusted clouds by resource-constrained data owners. (2) Scalable MPC Primitives for Large Datasets. Provably secure building blocks—such as oblivious shuffles, private joins, and sparse linear algebra routines—bridge the performance gap in privacy-preserving data pipelines at scale. (3) Verifiable ML without Retraining. Rather than proving each training step, a new proof-of-optimality framework certifies that a trained or fine-tuned model (e.g., LoRA adapters) satisfies desired properties, enabling efficient, auditable deployment without re-executing training. Together, these efforts aim to close the long-standing gap between privacy and efficiency, demonstrating that strong cryptographic guarantees and modern ML workflows can be reconciled through principled, application-aware design.
This project applies the latest cutting-edge Web3 technologies to design a Blockchain-Based Proof of Contribution (PoC) Framework aimed at improving efficiency, security, and transparency in managing research outputs and safeguarding intellectual property in collaborative settings. This framework uses the features of smart contracts on Ethereum and blockchain technology to guarantee transparency and accountability by automating the process involved in awarding the funds raised, verification of donations, and resolving disputes. After hashing a contribution cryptographically, every one of them stays on the blockchain, generating an irreversible record of its veracity. The application of IPFS in this makes decentralized and safe offchain storage possible, scalable, and intact. It has an automated dispute resolution process, which guarantees that the outcomes will be fair and not sent out for arbitration from outside. The framework deals with issues of trust, data security, and intellectual property in order to provide a safe and effective setting in which researchers can focus on creativity, knowing their work is safely documented, appropriately acknowledged, and protected from misuse.
This paper presents a comprehensive comparative analysis of two dominant blockchain consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), evaluated across seven critical metrics: energy use, security, transaction speed, scalability, centralization risk, environmental impact, and transaction fees. Utilizing recent academic research and real-world blockchain data, the study highlights that PoW offers robust, time-tested security but suffers from high energy consumption, slower throughput, and centralization through mining pools. In contrast, PoS demonstrates improved scalability and efficiency, significantly reduced environmental impact, and more stable transaction fees, however it raises concerns over validator centralization and long-term security maturity. The findings underscore the trade-offs inherent in each mechanism and suggest hybrid designs may combine PoW's security with PoS's efficiency and sustainability. The study aims to inform future blockchain infrastructure development by striking a balance between decentralization, performance, and ecological responsibility.
Blockchain technology has emerged as a revolutionary paradigm for secure, transparent, and tamper-resistant data management. It offers a decentralized ledger where transactions are validated and recorded across a distributed network of nodes, eliminating the need for centralized authorities. Despite its widespread adoption across diverse domains—such as finance, supply chain, healthcare, and digital identity—blockchain still faces significant challenges in ensuring complete security and privacy. This paper addresses these challenges by proposing a novel security and privacy algorithm designed specifically to enhance blockchain resilience against evolving threats. The proposed approach integrates hybrid cryptography, pseudonymous identifiers, and an optimized consensus mechanism to achieve a balanced trade-off between security, privacy, and computational efficiency. The hybrid cryptographic model combines symmetric and asymmetric encryption techniques to safeguard transaction data at multiple layers. Symmetric encryption ensures fast and secure data exchange, while asymmetric keys are used for identity verification and secure key distribution. To further strengthen user anonymity, the algorithm incorporates pseudonymous identity management, which replaces permanent public keys with dynamically generated pseudonyms. These pseudonyms are refreshed periodically to prevent link ability between consecutive transactions, ensuring that individual identities remain hidden even if certain nodes or data patterns are compromised. Additionally, the optimized consensus protocol enhances transaction validation efficiency by reducing redundant computations and improving synchronization among nodes. This approach minimizes latency and energy consumption while maintaining strong resistance against consensus-based attacks such as 51% or Sybil attacks. Extensive simulations and experimental evaluations were conducted to measure the algorithm’s performance under various network conditions and adversarial scenarios. The results demonstrate that the proposed model significantly improves transaction validation speed and reduces cryptographic overhead compared to traditional Proof-of-Work and Proof-of-Stake systems.
Blockchain has emerged as a robust foundation for decentralized trust, secure data sharing, and immutable record keeping. However, its inherently transparent architecture creates significant privacy challenges when applied in sensitive domains such as healthcare, finance, identity management, and IoT. Although privacy-preserving techniques including Zero-Knowledge Proofs (ZKPs), Attribute-Based Encryption (ABE), homomorphic encryption, ring signatures, mixers, and hybrid off-chain storage mechanisms have demonstrated partial effectiveness, they remain limited by high computational overhead, poor scalability, interoperability constraints, and regulatory complications. These challenges hinder the practical deployment of blockchain in real-world, data-intensive environments. This review examines key blockchain privacy issues and synthesizes major research contributions from contemporary literature. It further emphasizes the importance of hybrid privacy-preserving models to balance transparency, confidentiality, and storage efficiency. The analysis reinforces the relevance of solutions such as ChainGuard, a dual-chain architecture that maintains sensitive data on a private blockchain while using a public chain to store verifiable hash references. This approach directly mitigates the transparency–privacy conflict, storage inefficiencies, and cryptographic performance limitations identified across existing studies. The paper concludes by outlining research gaps and proposing future directions for scalable, interoperable, and regulation-aligned blockchain privacy systems.
Incident reporting systems are integral to maintaining accountability and transparency across critical domains such as cybersecurity, healthcare, and public governance. However, existing centralized mechanisms are prone to manipulation, data loss, and unauthorized modifications. This paper proposes 'IntegriChain', an intelligent and decentralized incident reporting framework that combines Blockchain technology and Artificial Intelligence (AI). The system ensures tamper-proof data storage through SHA-256 hashing and distributed ledger technology while leveraging AI for incident classification, anomaly detection, and risk prediction. This hybrid approach improves security, reliability, and efficiency in reporting workflows. The framework is designed to serve as a scalable solution applicable to multi-domain reporting systems where trust, immutability, and intelligent analysis are critical.
Blockchain, as a new distributed technology, plays important roles in various areas. But due to unsupervised transaction model of blockchain, Decentralized Application (DAPP) mainly focus on limited Decentralized Finance (DEFI) areas such as lending or trading, leaving enterprise and government application untouched. This paper propose a supervised transaction algorithm with identity privacy enabled on blockchain to tackle this problem. In essence, we encrypt transaction before its submission onto the chain, and execute transaction after supervisor's approval. The paper satisfies the compliance of regulation in finance and social application, and ensures that identity privacy of trader can be protected. In this way, blockchain can be adapted to broader domains without technology compromise.
Xin Liu, Anyang Qi, Lanying Liang, Dan Luo · 10 authors
In computer vision, the intersection determination of polygonal areas is utilized to segment different regions in an image and assist in detecting the boundaries of the regions. Moreover, the secure computation of the intersection area of polygons can solve the private calculation of geometric problems in machine learning. A security protocol under the semi-honest model was designed for the problem of secure computation of the intersection area of two polygons. This protocol adopts a new coding method and the Paillier homomorphic encryption algorithm. Aiming at the malicious behaviors that malicious participants may carry out in the semi-honest protocol, a secure computation protocol for the intersection area of polygons under the malicious model was designed by using methods such as hash function, cut-and-choose and zero-knowledge proof. The security of this protocol was proved, and its computational complexity and communication complexity were analyzed. Compared with the existing schemes, it is more efficient.
Cryptocurrencies are increasingly the subject of fake news, increasing risks for market stability and investor decisions. To address this issue, we propose a multimodal framework to detect fake cryptocurrency news using text, image, and sentiment features with BERT, Swin Transformer, and RoBERTa, respectively. We use multi-head attention to combine these features to ensure the complementarity of features from different modalities. The fused representations are passed into a fully connected layer for final classification. Experimental results show that this framework achieves better accuracy and reliability than unimodal and multimodal models for detecting cryptocurrency misinformation.
With the growing demand for secure medical data sharing and accurate insurance premium assessment, privacy, trust, and interoperability have become pressing challenges in healthcare digitalization. Existing solutions often suffer from centralized control, inflexible access policies, and weak privacy guarantees. To address these, we propose a blockchain-based, privacy-preserving scheme that integrates threshold proxy re-encryption and zero-knowledge proofs. Patients’ encrypted medical records are stored in private cloud environments, while access control is enforced through a threshold proxy re-encryption mechanism. Dynamic rekeying and delegation are supported through re-encryption key redistribution. For insurance verification, we generate zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) based on arithmetic circuits that represent the insurance policy requirements. These proofs are verified on-chain without revealing any underlying medical information. Security and performance analyses demonstrate the feasibility and efficiency of the proposed system. Experimental results show that our scheme supports sub-100 ms re-encryption cycles, reduces zero-knowledge proof generation time by over 90% compared to other zk-SNARK schemes, and imposes low on-chain computational overhead. These findings demonstrate the feasibility and potential suitability for real-world privacy-preserving medical data sharing and insurance evaluation.
<p>Document forgery remains a pervasive problem across education, government, and trade sectors. This paper presents a blockchain-based digital document verification system built on the Internet Computer Protocol (ICP). The approach computes SHA‑256 hashes of documents and anchors them to ICP canister smart contracts, ensuring integrity and non-repudiation without storing document contents. The system manages a registry of approved verifiers so that only trusted institutions can enroll documents. In evaluation with 15 documents (85–3025 KB) and five repeated trials per document, the prototype achieved an average verification time of 1.54 s and an accuracy of 99%. Compared with Ethereum-based baselines in prior work, the ICP-based design avoids gas fees and reduces verification latency. The proposed architecture supports future integration of zero-knowledge proofs (ZKP) to validate authenticity while preserving privacy.</p>
Open access
Blockchain Technology Applications and Security
Big Data and Digital Economy
Physical Unclonable Functions (PUFs) and Hardware Security
Smart contracts have significantly advanced blockchain technology, and digital signatures are crucial for reliable verification of contract authority. Through signature verification, smart contracts can ensure that signers possess the required permissions, thus enhancing security and scalability. However, lacking checks on signature usage conditions can lead to repeated verifications, increasing the risk of permission abuse and threatening contract assets. We define this issue as the Signature Replay Vulnerability (SRV). In this paper, we conducted the first empirical study to investigate the causes and characteristics of the SRVs. From 1,419 audit reports across 37 blockchain security companies, we identified 108 with detailed SRV descriptions and classified five types of SRVs. To detect these vulnerabilities automatically, we designed LASiR, which utilizes the general semantic understanding ability of Large Language Models (LLMs) to assist in the static taint analysis of the signature state and identify the signature reuse behavior. It also employs path reachability verification via symbolic execution to ensure effective and reliable detection. To evaluate the performance of LASiR, we conducted large-scale experiments on 15,383 contracts involving signature verification, selected from the initial dataset of 918,964 contracts across four blockchains: Ethereum, Binance Smart Chain, Polygon, and Arbitrum. The results indicate that SRVs are widespread, with affected contracts holding $4.76 million in active assets. Among these, 19.63% of contracts that use signatures on Ethereum contain SRVs. Furthermore, manual verification demonstrates that LASiR achieves an F1-score of 87.90% for detection. Ablation studies and comparative experiments reveal that the semantic information provided by LLMs aids static taint analysis, significantly enhancing LASiR's detection performance.
Software services are crucial for reliable communication and networking; therefore, Site Reliability Engineering (SRE) is important to ensure these systems stay reliable and perform well in cloud-native environments. SRE leverages tools like Prometheus and Grafana to monitor system metrics, defining critical Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for maintaining high service standards. However, a significant challenge arises as many developers often lack in-depth understanding of these tools and the intricacies involved in defining appropriate SLIs and SLOs. To bridge this gap, we propose a novel SRE platform, called SRE-Llama, enhanced by Generative-AI, Federated Learning, Blockchain, and Non-Fungible Tokens (NFTs). This platform aims to automate and simplify the process of monitoring, SLI/SLO generation, and alert management, offering ease in accessibility and efficy for developers. The system operates by capturing metrics from cloud-native services and storing them in a time-series database, like Prometheus and Mimir. Utilizing this stored data, our platform employs Federated Learning models to identify the most relevant and impactful SLI metrics for different services and SLOs, addressing concerns around data privacy. Subsequently, fine-tuned Meta's Llama-3 LLM is adopted to intelligently generate SLIs, SLOs, error budgets, and associated alerting mechanisms based on these identified SLI metrics. A unique aspect of our platform is the encoding of generated SLIs and SLOs as NFT objects, which are then stored on a Blockchain. This feature provides immutable record-keeping and facilitates easy verification and auditing of the SRE metrics and objectives. The automation of the proposed platform is governed by the blockchain smart contracts. The proposed SRE-Llama platform prototype has been implemented with a use case featuring a customized Open5GS 5G Core.
Luiz Eduardo Folly de Campos, Reinaldo Cézar de Morais Gomes
Trust on the internet is an essential pillar for online interactions, and blockchain technologies offer a new paradigm of trust based on data integrity and decentralization, enabling innovative solutions such as theWeb3 applications. This paper presents the experimental infrastructure for blockchain research and development currently being built within the ILIADA project at RNP, and its use for the development of new Web3 applications.
Ejiro U, Osiobe, Waleed A., Hammood, Safia, Malallah, Nyore E., Osiobe · 6 authors
Quantum mechanics principles underpin quantum computing, signaling a major shift in how we process information. While it offers immense processing power and potential advantages, it also presents significant challenges for the cryptocurrency industry. This sector has grown rapidly, supporting decentralized finance and empowering users worldwide, but it also attracts malicious actors looking to exploit its vulnerabilities. Traditional cryptography remains strong, yet increasingly sophisticated computational attacks threaten security. As the cryptocurrency market expands, quantum computing offers both opportunities, such as improved transaction security, and risks, like easier decryption for hackers. Understanding quantum technology’s benefits and challenges is crucial as it develops. Currently, data is protected by traditional cryptography, but future, more powerful quantum computers could weaken this security. This article explores potential uses of quantum computing in daily life and business, explains its functions simply, and discusses societal impacts. Its goal is to help students and general readers understand how quantum technology might transform our world through clear language and real-life examples. Topics include the basics of quantum computing, its present and future applications across industries, and its societal effects. We provide a thorough analysis of how quantum computing could reshape society through mathematical insights, practical examples, and future perspectives.
Ihunanya Udodiri Ajakwe, Victor Ikenna Kanu, Simeon Okechukwu Ajakwe, Dong‐Seong Kim
The Korean Emission Trading Scheme (K-ETS) is vital for reducing carbon emissions in South Korea. However, issues in transparency, security, and computational overhead limit its effectiveness. This work proposes an energy-efficient blockchain-based framework (eBCTC) to enhance the system with a decentralized blockchain architecture, Purechain. The framework leverages an improved consensus mechanism, the Proof of Authority and Association (PoA 2 ). This is to address key challenges in the current K-ETS, such as centralization, lack of transparency, and high energy consumption. The PoA 2 significantly reduces gas usage, with experimental results showing a 22 % reduction in gas consumption compared to traditional Proof of Work (PoW) and Proof of Authority (PoA) mechanisms. Also, PoA 2 recorded a ×6 and ×2 reduction in gas price compared to PoW and PoA. The system also achieves faster transaction finality and lower computational costs, with transaction costs reduced by up to 83 % across the key K-ETS activities, including emissions reporting, credit allocation, and trading. Also, the system achieved moderate throughput, high latency, doubling scalability, high reliability, and a high success rate compared with DPoS and PBFT based on transaction stress validation tests. With an improved smart contract, intelligent automation of key functions, the system achieved a high energy gain for improved incentives. The proposed framework not only enhances the scalability and transparency of K-ETS but also aligns with South Korea's carbon neutrality goals by minimizing the environmental impact of blockchain operations. This study provides a solid foundation for sustainable carbon trading systems and an accountable carbon economy, contributing to global efforts to combat climate change in achieving the 2050 net-zero carbon emissions goal. • Purechain PoA2 enables secure, low-energy carbon trading in K-ETS. • 22 % less gas usage and 83 % lower costs than PoW and PoA. • Smart contracts automate K-ETS compliance and incentives. • Improves scalability, transparency, and network reliability. • Supports South Korea's 2050 net-zero carbon goal.
As the application of Embodied Intelligence deepens within the Industrial Internet of Things (IoT) domain, traditional centralized trust schemes are increasingly unable to meet the demand for establishing efficient trust among heterogeneous devices, due to risks like single points of failure, auditing difficulties, and privacy leakage. To address these issues, this paper proposes a trust and privacy-preserving framework based on blockchain and Zero-Knowledge Proof (ZKP). The framework establishes a decentralized trust foundation using Hyperledger Fabric. On this foundation, a Decentralized Identity (DID) system is implemented through smart contracts, assigning a unique and verifiable identity anchor to each Embodied Intelligence device. Furthermore, to reconcile auditability and data privacy, the framework integrates ZKP technology. This technology enables edge devices to locally generate and submit on-chain proofs of operational compliance, facilitating transparent auditing without disclosing sensitive data. Finally, to transform trustworthy behavior records into a quantifiable metric, the framework designs a dynamic reputation assessment mechanism. This mechanism uses smart contracts to automatically analyze the verified on-chain behavioral history, continuously updating the reputation score for each Embodied Intelligence device. A smart factory case study demonstrates the framework's practical application, while performance evaluation on a physical testbed confirms its efficiency and scalability for real-time industrial control.
Existing Ethereum phishing scam detection methods based on graph neural networks focus on subgraph sampling while ignoring the global structure of the transaction network. When applied to graph structures, the sequence length of Transformers grows exponentially with the number of hops, leading to convergence issues for Transformer-based methods when applied to large-scale Ethereum data. To address these issues, this paper proposes a global graph transformer for Ethereum account detection, (GGTEAD). GGTEAD integrates node global attention extracted via dimensionality reduction algorithms with node local attention to strengthen transactional relationships between nodes. By introducing effective edge weighting strategies and anomaly scoring mechanisms, it significantly enhances detection capabilities for minority phishing accounts. This addresses the challenge of poor detection performance for phishing scam accounts within large-scale, heterogeneous Ethereum networks, providing valuable insights for IoT-blockchain integrated environments. The experimental findings, derived from two authentic Ethereum datasets (MulDiGraph and B4E) demonstrate that GGTEAD attains F1 scores of 84.36% and 71.22%, respectively, signifying enhancements of 5.53% and 2.88% over the prevailing state-of-the-art methods ScamSweeper and Bert4eth.
Based on the characteristics of blockchain such as decentralization, independence, security and anonymity, audit entities can explore the feasibility and application logic of its application in food security audits by leveraging technologies such as smart contracts, consensus mechanisms, asymmetric encryption and distributed ledgers. The article first analyzes the current situation and problems of food security auditing at both the practical and theoretical levels. Then, it constructs a logical framework for the application of blockchain technology in food security auditing by combining the advantages of blockchain technology, and specifically elaborates on the food security auditing process under blockchain technology. Finally, fully consider the problems faced by blockchain technology in its application and make prospects for its future development.
Raghu, Gurpartap Singh, S. Kannimuthu, Denis Amirtharaj · 6 authors
The industry growth of the digital currency market and the existence of the old banking system, in turn, has necessitated an urgent requirement of a hybrid financial infrastructure that will offer an effective, secure, and real-time interoperability. The current banking networks are founded on centralised books, which implies that interbank settlements are slow, untransparent, and involve high fraud risks. In contrast, the decentralisation and immutability of a public blockchain network are associated with the price of scalability, as well as regulatory and integration issues. Such loopholes will be bridged in this research through the introduction of a Decentralised Ledger Architecture of Hybrid Banking (DLHB), a consortium-based permissioned blockchain infrastructure, in which interbank fiat settlements are registered. Nevertheless, open blockchain networks will be used to effect digital currency settlements. Their connection will be based on cross-chain smart contracts that will facilitate the coordination of atomic, secure, and auditable transfers. It is built on recent cryptography-based capabilities, such as zero-knowledge proofs (ZKPs) and secure multi-party computation (SMPC), which can protect sensitive financial information and facilitate compliance. BFT protocols, which are optimised at the consortium layer, are classified under Byzantine Fault Tolerance to ensure that the technology meets both security and high throughput requirements. The public blockchain layer is optimised for the validation of Proof-ofStake (PoS). Simulation experiments indicate that the DL-HB framework is capable of supporting up to 85% higher transaction throughput, 60% reduced latency, and much more audit-friendly and fault-tolerant compared to the traditional banking systems and to the current blockchain solutions. The recommended architecture will allow co-existing between digital and fiat transactions on a transparent basis since the architecture will eradicate bottlenecks in operations, decrease the possibility of fraud, and real-time settlements. This paper proves that a multi-layered high-technical quality blockchain platform can redesign the hybrid banking systems as an efficient, secure, and scaled platform to integrate the emerging digital assets into the traditional financial systems without affecting regulatory compliance or stability of operations.
Joel Poncha Lemayian, Ghyslain Gagnon, Kaiwen Zhang, Pascal Giard
Ethereum leverages smart contracts (SCs) to power decentralized applications (dApps), with execution handled by the Ethereum virtual machine (EVM) within an Ethereum client. Other blockchain platforms, including Avalanche, Polkadot, Aurora, and Cardano, have also adopted the EVM. However, the performance of the EVM is often constrained by the limitations of general-purpose processors, a challenge that has been explored in the literature. This work aims to further address the limitation by proposing EVMx, a dedicated single-core SC execution engine implemented on a field programmable gate array (FPGA). EVMx follows a processor-like architecture inspired by the RISC philosophy. By exploiting the parallelism and high-speed processing capabilities of FPGA hardware, EVMx achieves a 61% to 99% reduction in execution time for commonly used operation codes compared to traditional central processing unit (CPU)-based environments. Furthermore, EVMx executes entire Ethereum blocks with a percentage reduction in execution time between 6% and 56% against comparable FPGA implementations and 98% to 99% compared to CPU-based EVMs in the literature. These results demonstrate the potential of EVMx to significantly accelerate SC execution and enhance the performance of EVM-compatible blockchains.