Follow blockchain research across journals, conferences, and preprint repositories.
5,430 results · page 63 of 227
Alex David S, M. J. Carmel Mary Belinda
No abstract is available for this record.
Valeria Nikolaenko, Sam Ragsdale, Joseph Bonneau, Dan Boneh
No abstract is available for this record.
Abdullah Husain
Distributed Ledger Technology (DLT) has been contentious since the emergence of blockchain in 2008. Security and compatibility with the personal data rights in the EU General Data Protection Regulation (GDPR) are among the controversies that have erupted. Thus, various studies have concluded that the decentralisation and immutability of DLT conflict with personal data rights. This dissertation illustrates that the DLT can be compatible with the GDPR personal data rights.
Linlin Li, Chungen Xu, Pan Zhang
No abstract is available for this record.
Xiaohui Yang, Kun Zhang
Data is regarded as a valuable asset, and sharing data is a prerequisite for fully exploiting the value of data. However, the current medical data sharing scheme lacks a fair incentive mechanism, and the authenticity of data cannot be guaranteed, resulting in low enthusiasm of participants. A fair and trusted medical data trading scheme based on smart contracts is proposed, which aims to encourage participants to be honest and improve their enthusiasm for participation. The scheme uses zero-knowledge range proof for trusted verification, verifies the authenticity of the patient’s data and the specific attributes of the data before the transaction, and realizes privacy protection. At the same time, the game pricing strategy selects the best revenue strategy for all parties involved and realizes the fairness and incentive of the transaction price. The smart contract is used to complete the verification and game bargaining process, and the blockchain is used as a distributed ledger to record the medical data transaction process to prevent data tampering and transaction denial. Finally, by deploying smart contracts on the Ethereum test network and conducting experiments and theoretical calculations, it is proved that the transaction scheme achieves trusted verification and fair bargaining while ensuring privacy protection in a decentralized environment. The experimental results show that the model improves the credibility and fairness of medical data transactions, maximizes social benefits, encourages more patients and medical institutions to participate in the circulation of medical data, and more fully taps the potential value of medical data.
Sepideh Avizheh, Reihaneh Safavi–Naini
No abstract is available for this record.
T. L. Quy, N. Ð. P. Trong, H. V. Khanh, H. L. Huong · 16 authors
No abstract is available for this record.
Huwida Said, Nedaa Baker Al Barghuthi, Sulafa Badi, Faiza Hashim · 5 authors
No abstract is available for this record.
Changrui Mu, Shafik Nassar, Ron D. Rothblum, Prashant Nalini Vasudevan
No abstract is available for this record.
Aneesh Sathe, Varun Prakash Saxena, P. Akshay Bharadwaj, S. Sandosh
No abstract is available for this record.
Huwida Said, Nedaa Baker Al Barghuthi, Sulafa Badi, Faiza Hashim · 5 authors
Blockchain technology holds significant promise for healthcare by enhancing the security and integrity of patient health records (PHRs) through decentralized storage and transparent access. However, it has substantial limitations, including problems with scalability, high transaction costs, privacy concerns, and intricate stakeholder access management. This study presents PHR-NFT, a novel framework that strengthens PHR privacy by utilizing Hyperledger Fabric and non-fungible tokens (NFTs) to address these issues. PHR-NFT improves privacy and communication by letting patients keep control of their medical records while permitting temporary, permission-based access by medical professionals. PHR-NFT offers a transparent solution that increases trust among healthcare stakeholders through the robust and decentralized architecture of the Hyperledger Fabric. This study demonstrates the viability and effectiveness of the PHR-NFT framework through performance evaluations focused on transaction latency, throughput, and security. This research has valuable implications for enhancing data privacy and security in healthcare practices and insightful information about blockchain-based healthcare systems.
Meenakshi Kashyap, Ravirajsinh Chauhan, Dhruvil Patel, Kishan Prajapati
Our world is, at a point in terms of the environment. The fashioned industrial approach, which heavily relies on centralized systems and resource-intensive computing is no longer sustainable. This document delves into a way to embrace Green Web 3.0, Decentralized AI and Edge Intelligence to drive the industry to-ward a more sustainable future. Green Web 3.0 challenges the energy nature of blockchain technology by utilizing eco-friendly protocols such as Proof of Stake which reduces energy consumption and lessens environmental impact. Similarly, Decentralized AI empowers distributed systems decreasing dependence, on data centers and promoting efficient resource utilization. Building on this foundation Edge Intelligence enables real-time decision making and data processing at the source reducing data transfer and optimizing energy efficiency. The combination of these technologies has the potential to revolutionize industries. Picture smart factories adjusting production in real-time supply chains supported by networks and renewable energy networks managed by intelligent edge devices.
Bo-Yan Liao, Jia-Wei Chang
No abstract is available for this record.
Sahaya Beni Prathiba, Yeshwanth Govindarajan, Vishal Pranav Amirtha Ganesan, Anirudh Ramachandran · 7 authors
Ensuring robustness against adversarial attacks is imperative for Machine Learning (ML) systems within the critical infrastructures of the Industrial Internet of Things (IIoT). This paper addresses vulnerabilities in IIoT systems, particularly in distributed environments like Federated Learning (FL) by presenting a resilient framework - Secure Federated Learning (SFL) specifically designed to mitigate data and model poisoning, as well as Sybil attacks within these networks. Sybil attacks, involving the creation of multiple fake identities, and poisoning attacks significantly compromise the integrity and reliability of ML models in FL environments. Our SFL framework leverages a Digital Twin (DT) as a critical aggregation checkpoint to counteract data and model poisoning attacks in IIoT’s distributed settings. The DT serves as a protective mechanism during the model update aggregation phase, substantially enhancing the system’s resilience. To further secure IIoT infrastructures, SFL employs blockchain-based Non-Fungible Tokens (NFTs) to authenticate participant identities, effectively preventing Sybil attacks by ensuring traceability and accountability among distributed nodes. Experimental evaluation within IIoT scenarios demonstrates that SFL substantially enhances defensive capabilities, maintaining the integrity and robustness of model learning. Comparative results reveal that the SFL framework, when applied to IIoT federated environments, achieves a commendable 97% accuracy, outperforming conventional FL approaches. SFL also demonstrates a remarkable reduction in loss rate, recording just 0.07 compared to the 0.14 loss rate experienced by standard FL systems. These findings highlight the efficiency and applicability of the SFL framework in enhancing data security and traceability within the IIoT ecosystem.
Subhranil Das, Rashmi Kumari, Raghwendra Kishore Singh, Dev Rishi · 5 authors
Zero Knowledge Proofs (ZKPs) have emerged as transformative cryptographic primitives, enabling a wide variety of modern privacy-preserving applications across various domains. This paper presents substantial advancements in contemporary ZKPs, exploring the latest trends in ZKP schemes, novel applications, and scalable ZKP-based systems. We provide an in-depth analysis of state-of-the-art ZKP structures, including zk-SNARKs, zk-STARKs, and the more recent zk-Rollup technologies. We examine the unique properties and use cases of these techniques, highlighting their potential to enhance privacy, security, and trust in digital systems. Our research into zk-Rollups demonstrates significant improvements in transaction throughput and gas efficiency, paving the way for the extensive deployment of modern privacy-preserving solutions. Furthermore, we showcase several innovative applications of ZKPs, such as privacy-preserving decentralized finance (DeFi) protocols, anonymous credentials, and secure multi-party computation. These use cases illustrate the transformative potential of advanced ZKPs to improve privacy and security across a range of digital domains. To foster wider adoption of modern ZKPs, we have developed and opensourced a collection of advanced tools and libraries that simplify the implementation of ZKP-based solutions. We also offer practical guidance and best practices for developers and researchers working in this field, aiming to accelerate the development and real-world impact of modern zero-knowledge proof systems.
Bernardo David, Rafael Dowsley, Anders Konring, Mario Larangeira
No abstract is available for this record.
Yang Han
In this study, a blockchain based federated learning system using an enhanced weighted mean vector optimization algorithm, known as EINFO, is proposed. The proposed EINFO addresses the limitations of federated averaging during global update and model training, where data is unevenly distributed among devices and there are variations in the number of data samples. Using a well-defined structure and updating the vector positions by local searching, vector combining, and updating rules, the EINFO algorithm maximizes the shared model parameters. In order to increase the exploration and exploitation capabilities, the model convergence rate is improved and new vectors are generated through the use of a weighted mean vector based on the inverse square law. To choose validators, miners, and to propagate new blocks, a delegated proof of stake based on the reliability of blockchain nodes is suggested. Federated learning is included into the blockchain to protect nodes from both external and internal threats. To determine how well the suggested system performs in relation to current models in the literature, extensive simulations are run. The simulation results show that the proposed system outperforms existing schemes in terms of accuracy, sensitivity and specificity.
Emanuele Giunta, Alistair Stewart
No abstract is available for this record.
Fenghong Zhang
No abstract is available for this record.
Surya Mathialagan, Spencer Peters, Vinod Vaikuntanathan
No abstract is available for this record.
Lu Lin, Lingyan Han, Liangmin Wang
No abstract is available for this record.
Mei Jiang, Yannan Li, Willy Susilo, Dung Hoang Duong
Energy-efficient proof-of-stake (PoS) consensus protocols in blockchain have gained much attention from academia and industry recently. Despite their potential advantages, PoS protocols have not been extensively deployed in the existing digital currency market due to inherent security concerns, e.g., long-range attacks. Such attacks enable an adversary to rewrite the entire transaction history of a blockchain, severely compromising its immutability. The puncturable signature provides an efficient solution against long-range attacks due to secret key leakage. More specifically, a signer can update the secret key with chosen messages selectively, while the public key is unchanged. Unfortunately, the existing puncturable signature schemes suffer from either updating the public key repeatedly or large key size, which makes them unsuitable for PoS protocols. To resolve these drawbacks, we adopt a different approach to performing key puncture operations and propose a generic puncturable signature construction from delegated (key-policy) constrained signatures. We present a concrete puncturable signature scheme over lattices that is proven secure based on the short integer solution (SIS) assumption in the standard model.
Sudhakaran Gajendran, Revathi Muthusamy, Krithiga Ravi, Omkumar Chandraumakantham · 5 authors
In this paper, a novel Elliptic Crypt with Secured Blockchain-backed Federated Q-Learning Framework is proposed to offer an intelligent healthcare system that mitigates the attacks and data misused by malicious intruders. Initially, the entered IoMT data is collected from publicly available datasets and encrypted using the Extended Elliptic Curve Cryptography (E_ECurCrypt) technique for ensuring the security. This encrypted data is fed as an input to the blockchain-powered collaborative learning model. Here, the federated Q-learning model trains the inputs and analyzes the presented attacks to ensure better privacy protection. Afterwards, the data is securely stored in decentralized blockchain technology. Subsequently, an effective Delegated Proof of Stake (Del_PoS) consensus algorithm is used to validate the proposed framework. The experiment is conducted using the WUSTL-EHMS-2020 dataset and the performances are analyzed by evaluating multiple matrices and compared to other existing methods. The performance of the proposed framework can be assessed using multiple matrices and the results will be compared to other existing methods. As a result, the proposed method has achieved 99.23% accuracy, 98.42% precision, 98.12% recall, 98.27% F1 score, 59080.506 average throughput, 59080.506 average decryption time 1.94 seconds and an average encryption time of 1.84 seconds and are superior to conventional methods.
Marlena Broniszewska, Wiktor B. Daszczuk, Denny B. Czejdo
No abstract is available for this record.