Incentivizing Honest Mining in Blockchain Networks: A Reputation Approach

Tang, Changbing; Wu, Lide; Wen, Guanghui; Zheng, Zhonglong

doi:10.1109/tcsii.2019.2901746

Cited by 33 publications

(20 citation statements)

References 21 publications

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“…The Adam Optimizer and RMSprop hint at over-fitting as there is a significant difference between the training and validation losses [ 100 , 101 ]. The Momentum Optimizer gives better results for validation loss as the loss decreases concerning the time [ 102 ].The voting classifier implements the aggregation of different classifiers such as Random Forest Classifier, SVC (support vector classifier), Logistic Regression and predicts the class probability for the highest votes [ 103 ]. Thus, the authors implemented the ensemble classifier technique and aggregated the aftermaths procured from this model, as shown in Appendix 3 [ 104 , 105 ].…”

Section: Proposed Improvement In Existing Solutionsmentioning

confidence: 99%

Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach

et al. 2022

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It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment.

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Section: Proposed Improvement In Existing Solutionsmentioning

confidence: 99%

Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach

et al. 2022

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“…For example, developing well-balanced incentive mechanisms for improving economic benefits is highly needed to encourage more users to implement FL training and blockchain mining. The authors in [91] implement a new reputation-based solution for the PoW computation in the blockchain, where mobile miners are incentivized to perform honest mining. A reputation-based algorithm is designed using a game theory to encourage users to join the mining process for extra profits with resource management awareness.…”

Section: Economic Issues In Flchainmentioning

confidence: 99%

Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges

Nguyen¹,

Ding²,

Pham³

et al. 2021

Preprint

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Mobile edge computing (MEC) has been envisioned as a promising paradigm to handle the massive volume of data generated from ubiquitous mobile devices for enabling intelligent services with the help of artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training in a single entity, e.g., an MEC server, which is now becoming a weak point due to data privacy concerns and high data communication overheads. In this context, federated learning (FL) has been proposed to provide collaborative data training solutions, by coordinating multiple mobile devices to train a shared AI model without exposing their data, which enjoys considerable privacy enhancement. To improve the security and scalability of FL implementation, blockchain as a ledger technology is attractive for realizing decentralized FL training without the need for any central server. Particularly, the integration of FL and blockchain leads to a new paradigm, called FLchain, which potentially transforms intelligent MEC networks into decentralized, secure, and privacy-enhancing systems. This article presents an overview of the fundamental concepts and explores the opportunities of FLchain in MEC networks. We identify several main topics in FLchain design, including communication cost, resource allocation, incentive mechanism, security and privacy protection. The key solutions for FLchain design are provided, and the lessons learned as well as the outlooks are also discussed. Then, we investigate the applications of FLchain in popular MEC domains, such as edge data sharing, edge content caching and edge crowdsensing. Finally, important research challenges and future directions are also highlighted.

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“…Whereas works of Andoni et al 2019, Tang et al (2019), Wang et al (2020), and Dinh et al (2018) review the state of the art, improve consensus protocols, and evaluate existing systems, our work presents a framework for identity management and auditing. BFIF framework presented here could be implemented with different technologies discussed in those reviewed works.…”

Section: Related Workmentioning

confidence: 99%

Blockchain-based federated identity and auditing

El-Gayyar¹,

ElYamany²,

Grolinger³

et al. 2020

IJBC

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A federated identity is a single identity that enables users to access multiple services across a network of business parties. Such identities are subject to various threats and attacks and face diverse challenges including identity leaks, centralised management, auditing limitations, and long breach investigation processes. This paper proposes a framework aimed at automating and decentralising the generation and auditing of a robust and secured blockchain-based federated identity in a marketplace. Business parties participating in the marketplace form the nodes of a distributed blockchain network and participate in the creation of federated identities. Users of this network can access services provided by any one of the participating parties using a single federated identity. All transactions are fully audited in the blockchain, meaning that participating parties can monitor access to their service and users can trace the use of their identities. The proposed framework has been evaluated using two blockchain technologies (Ethereum and Hyperledger Fabric) to measure its performance in public and permissioned blockchain environments.

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Incentivizing Honest Mining in Blockchain Networks: A Reputation Approach

Cited by 33 publications

References 21 publications

Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach

Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach

Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges

Blockchain-based federated identity and auditing

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