Blockchain-Based Decentralized and Lightweight Anonymous Authentication for Federated Learning

Fan, Mochan; Zhang, Zhipeng; Li, Zonghang; Sun, Gang; Yu, Hongfang; Guizani, Mohsen

doi:10.1109/tvt.2023.3265366

Cited by 10 publications

(2 citation statements)

References 39 publications

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“…This requirement aligns well with the inherent characteristics of blockchain technology. Recent studies have used the blockchain's distributed storage architecture as the foundational framework for FL [69][70][71][72][73][74][75][76][77][78][79]. By designing protocols on the upper layer of the blockchain, they implement the task of model aggregation running for clients.…”

Section: Blockchain-based Federated Learningmentioning

confidence: 99%

A Survey on Blockchain-Based Federated Learning

Wu,

Ruan,

et al. 2023

Future Internet

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Federated learning (FL) and blockchains exhibit significant commonality, complementarity, and alignment in various aspects, such as application domains, architectural features, and privacy protection mechanisms. In recent years, there have been notable advancements in combining these two technologies, particularly in data privacy protection, data sharing incentives, and computational performance. Although there are some surveys on blockchain-based federated learning (BFL), these surveys predominantly focus on the BFL framework and its classifications, yet lack in-depth analyses of the pivotal issues addressed by BFL. This work aims to assist researchers in understanding the latest research achievements and development directions in the integration of FL with blockchains. Firstly, we introduced the relevant research in FL and blockchain technology and highlighted the existing shortcomings of FL. Next, we conducted a comparative analysis of existing BFL frameworks, delving into the significant problems in the realm of FL that the combination of blockchain and FL addresses. Finally, we summarized the application prospects of BFL technology in various domains such as the Internet of Things, Industrial Internet of Things, Internet of Vehicles, and healthcare services, as well as the challenges that need to be addressed and future research directions.

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Section: Blockchain-based Federated Learningmentioning

confidence: 99%

A Survey on Blockchain-Based Federated Learning

Wu,

Ruan,

et al. 2023

Future Internet

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“…While AES-GCM is renowned for its rapid and efficient encryption, its effectiveness hinges on a critical operational requirement: the non-repetition of key-nonce pairings. This seemingly straightforward condition is crucial; a single lapse in AES-GCM can compromise the entire security framework [17][18][19][20]. Beyond specific algorithmic weaknesses, a broader challenge in cryptography is achieving a balance among confidentiality, authenticity, and integrity.…”

Section: Introductionmentioning

confidence: 99%

A Holistic Secure Communication Mechanism Using a Multilayered Cryptographic Protocol to Enhanced Security

Wang,

Tabassum

2024

CMC

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In an era characterized by digital pervasiveness and rapidly expanding datasets, ensuring the integrity and reliability of information is paramount. As cyber threats evolve in complexity, traditional cryptographic methods face increasingly sophisticated challenges. This article initiates an exploration into these challenges, focusing on key exchanges (encompassing their variety and subtleties), scalability, and the time metrics associated with various cryptographic processes. We propose a novel cryptographic approach underpinned by theoretical frameworks and practical engineering. Central to this approach is a thorough analysis of the interplay between Confidentiality and Integrity, foundational pillars of information security. Our method employs a phased strategy, beginning with a detailed examination of traditional cryptographic processes, including Elliptic Curve Diffie-Hellman (ECDH) key exchanges. We also delve into encrypt/decrypt paradigms, signature generation modes, and the hashes used for Message Authentication Codes (MACs). Each process is rigorously evaluated for performance and reliability. To gain a comprehensive understanding, a meticulously designed simulation was conducted, revealing the strengths and potential improvement areas of various techniques. Notably, our cryptographic protocol achieved a confidentiality metric of 9.13 in comprehensive simulation runs, marking a significant advancement over existing methods. Furthermore, with integrity metrics at 9.35, the protocol's resilience is further affirmed. These metrics, derived from stringent testing, underscore the protocol's efficacy in enhancing data security.

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Revocable identity-based designated verifier proxy re-signature with signature evolution

Zhang,

Sun,

et al. 2025

Computer Standards & Interfaces

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Blockchain-Based Decentralized and Lightweight Anonymous Authentication for Federated Learning

Cited by 10 publications

References 39 publications

A Survey on Blockchain-Based Federated Learning

A Survey on Blockchain-Based Federated Learning

A Holistic Secure Communication Mechanism Using a Multilayered Cryptographic Protocol to Enhanced Security

Revocable identity-based designated verifier proxy re-signature with signature evolution

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