Follow blockchain research across journals, conferences, and preprint repositories.
97,057 results · page 403 of 4,045
Lvbin Xu, Lei Wu, Xingyu Wu, Zhaoyu Su
No abstract is available for this record.
Damanpreet Kaur, Sarabjeet Kaur
The emergence of blockchain technology represents a revolutionary breakthrough that specifically transforms computer networking operations. Security is strengthened by integrating blockchain technology with networking infrastructure, and decentralization functions become more effective and efficient. The paper examines recent developments that unite blockchain with FL (Federated Learning) through an analysis of ways blockchain technologies support FL functions while resolving its operational obstacles. The research examines how innovation develops through methods of decentralization coordination, along with incentive systems, solutions for privacy protection and scalability, and models of governance. The review paper investigates the responsibility of security adaptation and decentralized knowledge for enhancing blockchain-based networking systems. The research examines major obstacles along with the advantages and outlines prospective research pathways. Academic researchers with expertise in computer science evaluated blockchain, and it received evaluations from specialists in finance and healthcare as well as supply chain management fields.
Muntasir Mamun, Qin Wang, Jiaying Qian, Gao Li
No abstract is available for this record.
Gauthier-Maxence, Céline, Milcent, Carine
The use of blockchain in healthcare is attracting growing interest due to its ability to guarantee the integrity, traceability, and transparency of sensitive data processing. However, its integration into a legal environment strictly regulated by the GDPR and French healthcare data law faces major tensions: immutability versus the right to erasure, decentralization versus legal responsibility, transparency versus medical confidentiality. After reviewing the legal obstacles, this article proposes a series of regulatory adjustments aimed at making the use of distributed ledgers in healthcare legally viable. It calls for dedicated governance, a functional interpretation of rights, and regulation by design in order to reconcile technological innovation with the protection of fundamental rights.
Ansh Goyanka, Shlok Khairnar, Harsh Jaisingpure, Anuradha Yenkikar · 6 authors
In the rapidly evolving decentralized finance landscape-where retail traders struggle to compete alongside institutional traders, this project provides a dedicated, Artificial Intelligence (AI) -powered trading assistant equipped with the ability to unlock professional(pro) trading strategies without any need to code. By increasingly integrating a trained machine learning model, where the database consists of over 1 million Solana memecoin data points, with a conversational AI interface and on-demand, blockchain-level analytics, the system enables users to trade on tokens such as Base Mainnet or Solana assets with one natural language command. The AI queries live Decentralised Exchange (DEX) data through The Graph protocol, determines matches, and generates$\mathbf{1 5}$-minute price predictions using the Long-Short Term Memory (LSTM) neural network. The AI performs all processes autonomously, allowing it to manage wallets and make trades using an independently validated Return of Interest (ROI) of 30.57 %. The platform simplifies candlestick patterns, liquidity-level data, and market indicators into chat-based task workflows over a 3-step process. The model is created with TensorFlow for predictive analytics, Collateralised Debt Position (CDP) Agents for engagement, and uses Coinbase's Software Development Kit (SDK) for trading. This research proves that AI can level the playing field for casual traders using complex algorithmic trading strategies and data through simple human engagement.
Reena Lenka, Ronak Gupta, Vanishree Pabalkar, Jaya Chitranshi
No abstract is available for this record.
Bin Ma, Zijie Gao, Ruihe Ma, Yongjin Xian · 5 authors
No abstract is available for this record.
Z. Y. Zhu, Chenzhe Yang, Xiaofeng Ma
No abstract is available for this record.
Xingyun Zheng, Jianrong Lu, Zhenqing Gu, Jianhai Chen
No abstract is available for this record.
S. N. Jain, Rupprashik A. Khare, Samyak Lahire, Chaitali Patil
There are more than over 23,000 cryptocurrencies” in existence which represent one of the biggest unregulated markets across the globe. Currently, each cryptocurrency utilises distinct public-private key and public address formats. This diversity complicates the investigator's task of tracing and verifying a suspect's involvement in cryptocurrency-related crimes. Moreover, law enforcement agencies such as the police frequently seize digital devices, generating digital disk images for investigation which is a time consuming process. Thus, the investigation of cryptocurrencies has emerged as a major challenge. for law enforcement agencies across the world. Our proposed solution “CryptoKalp” aims to combat cybercrimes involving cryptocurrencies. The essence of our proposed tool is encapsulated in its name: “CryptoKalp.” The name is a fusion of two significant terms - “crypto,” signifying cryptocurrency, and “kalp,” a Sanskrit word representing investigation [1]. Our proposed tool makes the investigation process quicker by taking digital images, strings, various formats of files and folders as input and utilising a comprehensive database of regular expressions covering various cryptocurrency public-private key and address formats. Currently, our tool successfully identifies Bitcoin public-private keys and addresses from text files, with future plans outlined expanding its capabilities for Ether, Tether, Monero, Dash and Dogecoin. Investigating officers can access the system's user-friendly interface and the database containing login credentials, cryptocurrency formats, and history of searched strings, which enables officers to track a specific criminal associated with a series of crypto transactions. Police can confirm whether a suspect is involved in cryptocurrency transactions and take necessary actions. Our solution will aid law enforcement authorities in combating cybercrime.
Hengxin Lei, Thein Lai Wong, Tong Ming Lim, XiangFu Zhao · 6 authors
Smart contracts manage billions of dollars' worth of digital assets. Once vulnerabilities are exploited, they may lead to fund theft, transaction rollback, or asset freezing. The current machine learning based smart contract vulnerability detection methods have poor performance and high time complexity in detecting sparse labeled data. We propose a smart contract vulnerability detection model for supervised Bigram-Principle Component Analysis (Bi-PCA). Supervising Bi-PCA can utilize labeled vulnerability data for supervised dimensionality reduction. The supervised Bi-PCA model can utilize the information from these additional labels to accurately extract more interpretable potential structures. This detection model is universal and can be used in industrial scenarios on all labeled datasets. The experimental results show that it has high accuracy and recall while maintaining the feature of the original label data.
Qing Xue, Zhijie Zhong, Sicheng Hao, Zewei Lin · 6 authors
No abstract is available for this record.
Purnima Ahirao, Yogita Borse, Deepti Patole, Shreshtha Agarwal · 6 authors
The Denial-of-service (DoS) attacks pose a significant risk in smart contracts. They often arise from unexpected failures in external calls. This papers thoroughly examines DoS vulnerabilities in smart contracts and looks at effective ways to reduce these risks. The authors explore contract scenarios that are vulnerable to refund-failure DoS attacks. In these cases, bad actors can disrupt contract operations. The experiments show that some methods completely defend against these attacks, but they might some with a cost and hamper overall user experience. Other methods find a middle ground, improving security and usability by improving DoS and re-entrancy risks without interfering with single interaction flows. The research provides a solid evaluation for DoS mitigation strategies and offers practical advice for developing secure smart contracts.
Iflah Aijaz, Roohie Naaz Mir
Introduction: Ensuring secure and efficient identity management is crucial in an era where digital identities support numerous online services. Traditional Identity Management Systems (IDMS) face challenges such as data breaches, lack of user autonomy, and centralization risks, necessitating the exploration of decentralized alternatives. The review paper assesses blockchain-based IDMS as a potential solution, examining its benefits, challenges, and the role in improving security and privacy. Methods: This study conducts a systematic literature review of recent advancements in blockchain- based IDMS, drawing from peer-reviewed sources published between 2017 and 2024. The analysis focuses on security mechanisms, integration challenges, technological innovations, and their implications for digital identity management. Results: Blockchain-based IDMS offer many significant advantages, like enhanced security and privacy protection of identity data by the users. However, many challenges remain ahead, including the issue of scalability, interoperability with existing systems, and issues of regulatory acceptance. The advancements in this space are driven forward by recent innovations, such as zero-knowledge proofs and decentralized identifiers. Discussion: The review highlights blockchain’s transformative potential in addressing the flaws of traditional IDMS. While it offers notable improvements in privacy and user autonomy, successful real-world deployment requires overcoming technical and legal hurdles. These include adapting to existing standards and gaining acceptance among regulatory bodies. Conclusion: Blockchain technology has the potential to revolutionize digital identity management by overcoming several traditional IDMS limitations. While promising, further research is needed to overcome integration challenges and ensure regulatory compliance for real-world deployment.
Jian Bai, Junqiao Wang, Guishan Dong, Hongwei Li · 5 authors
No abstract is available for this record.
Rahul Konapure, Shankar D. Nawale
The modern pharmaceutical supply chain needs robust transparent systems to protect drugs authenticity across all distribution stages because of rising counterfeit medicine threats. This paper develops an Internet of Things (IoT) blockchain framework which implements smart contracts for complete pharmaceutical product traceability together with drugs verification. The proposed solution architecture measures its performance through scalability, latency and throughput metrics. we developed a custom smart contract through optimized state control logic and low gas costs and efficient batch processing functions. The architecture ensures high-throughput operation because it keeps transaction processing times short for large transaction volumes. The system evaluation shows latency results between$\mathbf{1 8 0} \boldsymbol{-} \mathbf{2 5 0}$milliseconds with throughput results between 50–60 transactions per second and batch processing capabilities between 1,000 and 10,000 transactions which demonstrates its scalability and responsiveness. The framework offers an effective solution against counterfeit drugs using secure blockchain execution of IoT device data while preserving supply chain integrity and transparency and maintaining operational efficiency.
D Zhang, Hao Song, Zhiqing Ma
No abstract is available for this record.
Ping Wang, Fei Tang, Ankui Jing, Lei Liu · 6 authors
In vehicular ad-hoc networks (VANET), federated learning enables vehicles to collaboratively train global models for intelligent transportation without sharing raw data. However, global model training faces various potential risks, such as identity leakage, privacy inference, and malicious attacks, due to the dynamic network structure and untrusted wireless communication of VANET. To address these issues, a robust authentication mechanism for federated learning must be achieved to ensure the trustworthiness of model parameters. In this paper, we propose an efficient and privacy-preserving authentication scheme with lightweight encryption for federated learning in VANET using self-sovereign identity (SSI), called Fed-EALE. Fed-EALE constructs Merkle pseudonym identity trees with the aid of decentralized identifiers. Vehicle participants use unlinkable pseudonyms to achieve privacy protection. Fed-EALE utilizes verifiable credentials and zero-knowledge proof to build the authentication protocol to ensure the authenticity and integrity of model parameters from anonymous vehicles. In addition, to accurately identify and eliminate malicious participants in anonymous communications, Fed-EALE can track and recover the real identities of malicious vehicles. We perform a security analysis of Fed-EALE. Performance evaluations indicate that Fed-EALE reduces authentication overhead by approximately 76% compared to state-of-the-art protocols, while maintaining high stability and scalability in VANET.
Sen Wang
Real world asset tokenization (RWA) introduces programmable finance on chain tools to the market, while bringing cash like returns. This paper focuses on token treasury bond funds to explore whether they are re anchoring the yield benchmark of decentralized finance (DeFi). The key entry point of the study is to build a de facto "interest rate corridor", which is formed by DeFi's stable monetary loan interest rate around the volatility of token treasury bond yield. The research results show that due to the widespread risk exposure in the tokenized currency market, DeFi USD returns have gradually converged towards short-term interest rate benchmarks. What is more noteworthy is that its stay time in the narrow corridor centered on the yield of token treasury bond is significantly prolonged. This re anchoring effect not only narrows the long-standing divergence between cryptocurrency native interest rates and monetary policy benchmarks, but also reshapes the incentive mechanism for liquidity supply, and further tightens the integration channels between on chain markets and traditional fixed income markets on this basis.
Muzamil Rashid, Vishal Sarin, Abu Bashar
Digital currencies such as cryptocurrencies and central bank digital currencies (CBDC.s) have transformed the financial transactions and economies across the globe. The monetization of such digital assets hinges on data driven strategies, with block-chain analytics, market trends, and economic indicators. This paper explores direct and indirect revenue models, focusing on trading, stacking, decentralized finance (DeFi), and block-chain analytics. Additionally, it examines regulatory and ethical challenges while presenting case studies of successful monetization strategies. The research highlights how businesses and investors can utilize big data and AI-driven insights to maximize profitability in the digital currency market. The study also explores emerging trends in digital currency adaption and discusses potential risks associated with these innovative financial tools, including regulatory interventions, technological advancements, and security threats.
Fabian Hensel, Avik Banerjee, Elmira Ebrahimi, Stefan Schulte
Solidity smart contracts are widely used to implement decentralized applications. However, their development remains challenging due to the language’s domain-specific complexity, the immutability of deployed contracts, which prevents post-deployment fixes, and the high risk of introducing security-critical vulnerabilities. While Large Language Models (LLMs) have advanced code generation across general domains, they often struggle to meet the structural and security-specific demands of smart contract development. Therefore, this paper presents a domain-adapted code completion model trained on 22,000 labeled code constructs extracted from Solidity contracts. The model is built on a transformer-based architecture and fine-tuned using Quantized Low-Rank Adaptation (QLoRA), a parameter-efficient method. The dataset is processed to highlight secure coding patterns and structural semantics, enabling the model to learn from both preceding and succeeding contexts. Evaluation using perplexity, the Bilingual Evaluation Understudy (BLEU) score, and the Metric for Evaluation of Translation with Explicit Ordering (METEOR) shows significant improvements with consistent gains across all three metrics compared to the base model. These results demonstrate that targeted adaptation of language models can significantly enhance coding support in Solidity smart contracts.
Vedansh Khatri, Vikas Garg, Rohit Pandey, Praveen Pachauri
The barriers toward the economy for underbanked communities are made much deeper due to their access to formal financial services. In recent years, mobile infrastructure and blockchain technology have opened new corners for the establishment of digital identity systems that show promise enabling secure and verifiable identity for greater access to finance. This study contrasts the systems through very rigorous casework in India and Uganda, and a technical comparison of mobile-based concurrent mobile and blockchain-enabled self-sovereign identity (SSI) frameworks. Observations from survey data supplement them by indicating trends on user adoption and challenges. Results indicate that digital identities give an additional entry into financial services anywhere between 20 to 30 percent for the under banked. Although they are the most affected by resistance posed by tried and tested issues such as low digital literacy, lack of infrastructural facilities, trust deficit among consumers, and so on. A hybrid digital identity framework with Zero-Knowledge Proofs (ZKPs) is thus proposed, which combines the widely accepted benefits of mobile systems with the enhanced privacy and data protection features offered by the blockchain technology. The model is constructed based on affordability, interoperability, and user agency; thus, it is scalable and inclusive to the economic realities faced by sub-Saharan Africa and South Asia.
K. Myrzabekkyzy, G. Lukhmanova, B. Dosanov, A. Bolganbayev · 5 authors
This article, within the context of modern financial technology development, analyzes the impact of decentralized finance (hereafter - DeFi) on the traditional financial system and its criminal-risk aspects. The study aims to describe DeFi operating mechanisms (decentralized architecture and smart contracts), systematize the directions of change in banking, lending, insurance, and investment services, and identify the main types of misconduct and fraud while proposing preventive measures. The paper clarifies DeFi’s operational features, the role of smart contracts, and the nature of decentralization, and examines DeFi’s position across traditional financial service segments. Types of offenses and fraudulent schemes occurring on DeFi platforms are identified, and prevention measures are proposed. A comparison between DeFi and traditional finance is provided, highlighting key advantages and disadvantages and offering recommendations to reduce criminal risks.
Leandro Rometsch, Philipp-Florens Lehwalder, Anh-Tu Hoang, Dominik Kaaser · 5 authors
Self-Sovereign Identity (SSI) frameworks enable individuals to receive and present digital credentials in a user-controlled way. Revocation mechanisms ensure that invalid or withdrawn credentials cannot be misused. These revocation mechanisms must be scalable (e.g., at national scale) and preserve core SSI principles such as privacy, user control, and interoperability. Achieving both is hard, and finding a suitable trade-off remains a key challenge in SSI research.This paper introduces UPPR, a revocation mechanism for One-Show Verifiable Credentials (oVCs) and unlinkable Anonymous Credentials (ACs). Revocations are managed using percredential Verifiable Random Function (VRF) tokens, which are published in a Bloom filter cascade on a blockchain. Holders prove non-revocation via a VRF proof for oVCs or a single Zero-Knowledge Proof for ACs. The construction prevents revocation status tracking, allows holders to stay offline, and hides issuer revocation behavior. We analyze the privacy properties of UPPR and provide a prototype implementation on Ethereum. Our implementation enables off-chain verification at no cost. On-chain checks cost 0.56–0.84 USD, while issuers pay only 0.00002–0.00005 USD per credential to refresh the revocation state.