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2,071 results · page 8 of 87
Rajkumar Soni, Manish Kumar Thukral, Neeraj Kanwar
No abstract is available for this record.
Ruonan Chen, Yang Zhang, Dawei Li, Yizhong Liu · 8 authors
Blockchain and cryptocurrencies are developing rapidly, and the scalability issue has become a constraint on their practical application and development. Off-chain payment channel is an effective solution to the scalability problem of blockchain. Currently, various payment channel protocols have been proposed. However, privacy issues are vital in payment channels. Existing works that consider privacy issues mainly focus on payment channel networks and payment channel hubs, while there is little work on two-party and multi-party channels. In addition, many existing payment channel works that consider privacy protection fix the transaction amounts to ensure the hiding of payment relationships or rely on smart contracts, which will hinder the practical application of payment channels. In this work, we propose a two-party privacy-preserving payment channel protocol that is compatible with Bitcoin (TBPChannel), achieving value privacy and unlinkability, while supporting variable transaction amounts. On this basis, we propose a privacy-preserving multi-party payment channel protocol (MBPChannel), which removes the role of untrusted operators in previous multi-party settings and further achieves robustness. We formally model the protocols in the universal composability framework and prove the security. Finally, we implement the protocols and provide a performance evaluation. The results demonstrate the scalability and practicality of our protocols. Compared to current protocols, even though we use privacy-preserving methods, our protocols are still efficient and applicable in practice.
Jonathan Bootle, Vadim Lyubashevsky, Antonio Merino-Gallardo
No abstract is available for this record.
Amrutanshu Panigrahi, Abhilash Pati, Santosh Reddy Addula, Ashis Pati · 6 authors
No abstract is available for this record.
Mahmoud Darwich, Magdy Bayoumi
No abstract is available for this record.
István András Seres, Noemi Glaeser, Joseph Bonneau
No abstract is available for this record.
Yifan Li
No abstract is available for this record.
Mingdong Tang, Xingyu Feng, Weili Chen
No abstract is available for this record.
Natasha Wanjari, Pratiksha Chafle, Rahul Moriwal
No abstract is available for this record.
Deshao Liu, Abubakar Bello
No abstract is available for this record.
هالة بهجت عبدالوهاب, زينب حسن كطوف
No abstract is available for this record.
Foteini Baldimtsi, Konstantinos Kryptos Chalkias, François Garillot, Jonas Lindstrøm · 10 authors
No abstract is available for this record.
Z. Chen, Jianyong Yu, Yulong Wang
No abstract is available for this record.
Rahul Pitale, Kapil Tajane, Vaidehi Bhonge, Vijay Chaure · 6 authors
No abstract is available for this record.
Pradeep Chintale, Anirudh Khanna, Ayisha Tabbassum, Saigurudatta Pamulaparthy Venkata · 5 authors
No abstract is available for this record.
Marcela Lupu, Iulian Aciobăniţei
No abstract is available for this record.
Quang-Ha Tran, Ba-Lam Do
No abstract is available for this record.
Kundu Chen, Jie Luo
No abstract is available for this record.
Jingbo Guan
To address the issues of excessive centralization and difficulties in cross-domain identity authentication in traditional cross-domain authentication schemes, an anonymous cross-domain authentication framework based on blockchain smart contracts is proposed. The framework uses Pedersen commitments to construct non-interactive zero-knowledge proofs, enabling users to achieve anonymous identity authentication with low communication overhead. Blockchain smart contracts are employed to record information about users and domains, realizing decentralized information storage. Experimental results show that the proposed scheme effectively reduces the level of centralization in cross-domain authentication and improves both the efficiency and security of cross-domain identity authentication.
V. N. Krishna Addepalli, Jainam Purushotam Patel
The abstract highlights the potential of integrating blockchain into 5G networks and the Metaverse and proposes an enhanced blockchain protocol for various applications. It emphasizes the transformative nature of 5G and blockchain technologies and their ability to revolutionize industries. It also discusses the capabilities of blockchain, such as smart contracts and decentralized storage, and the opportunities it presents for in- novative 5G services. It also addresses the challenges and open research problems in this domain. Furthermore, it explores the application of blockchain in the Metaverse, focusing on security, privacy, and scalability concerns. The proposed innovation aims to improve the blockchain protocol to effectively support 5G, Web3, Edge Computing, Metaverse, and many more applications. It prioritizes immutability, confidentiality, and availability and offers advantages to interaction and digital experiences. The objective is to create a protocol that meets diverse industry requirements while considering different approaches to achieve its goals.
Shagufta Henna, Mohamed Amjath
Detecting front-running attacks in Ethereum blockchain transactions is crucial for maintaining security and integrity within decentralized ecosystems. However, existing models struggle to accurately model the complex distributions inherent in tabular data, particularly in the presence of class imbalance and mode collapse. This paper leverages the potentials of Conditional Tabular Generative Adversarial Networks and PacGAN, called a Conditional Packing GAN (cPacGAN), to address these challenges. cPacGAN effectively generates synthetic data that closely mimics the distribution of real transactions, thereby augmenting the dataset and improving the performance of front-running attack detection. PacGAN mitigates mode collapse by incorporating packed samples in the discriminator, improving the diversity of generated samples and improving the stability of the training process. Through experimental evaluations of a real-world Ethereum transactions dataset, cPacGAN demonstrates improved performance across all selected machine learning classifiers, particularly augmenting the effectiveness of Tabular Neural Networks (TabNet).
Duc Bui Tien, Nam Tran Ba, Hong Khanh Vo
Traditional voting systems face numerous challenges, including security vulnerabilities, transparency issues, and operational inefficiencies, which undermine public confidence in electoral processes.Blockchain technology offers a promising solution with its immutable, decentralized, and cryptographically secure framework, addressing these critical issues.This paper presents a blockchainbased voting system implemented across multiple Ethereum Virtual Machine (EVM) platforms, including Binance Smart Chain, Fantom, Polygon, and Celo.The system leverages smart contracts for secure vote management and Non-Fungible Tokens (NFTs) for voter authentication, ensuring the uniqueness and authenticity of each vote.Our research includes a comprehensive evaluation of the system's performance, focusing on transaction costs, processing speed, and scalability.The findings demonstrate the potential of blockchain technology to efficiently handle large volumes of electoral data while maintaining security and integrity, thereby enhancing the reliability and transparency of voting systems.
M. Albrecht, Kamil Doruk Gür
No abstract is available for this record.
Mohamadsajad afkhami, Mahmoud baghani, Amir toranjsimin, Hamid‐Reza Mahrooghi
This article introduces a secure and efficient framework for the storage and validation of medical images using the Ethereum blockchain platform, incorporating the decentralized capabilities of the Interplanetary File System (IPFS) and digital signature methodologies employing zero watermarking. Medical images are critical to patient diagnosis and treatment, necessitating robust security measures, especially during transmission over insecure networks. Our approach utilizes chaotic sequences and transformations through Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD) to create a digital signature that ensures the integrity and privacy of the images without modifying their content. The solution encrypts medical images before storing them in IPFS with their corresponding digital signatures to safeguard confidentiality. Upon access, the images are decrypted and their signatures are checked to confirm their integrity. The effectiveness of this methodology is demonstrated by its strong resilience to network disturbances and potential security threats, achieving an average Normalized Correlation (NC) value of 0.97. This performance underscores the potential of integrating advanced cryptographic techniques with blockchain technology to enhance the security of medical image data.