Blockchains

18 pages, 577 KiB

Open AccessArticle

Toward a Blockchain-Based, Reputation-Aware Secure Transactive Energy Market

by Peng Zhang, Peilin Wu, Yuhong Liu, Ye Chen, Yuanliang Li, Jun Yan and Mohsen Ghafouri

Blockchains 2024, 2(1), 61-78; https://doi.org/10.3390/blockchains2010004 - 8 Mar 2024

Cited by 3 | Viewed by 1316

The rapid expansion of transactive energy has transformed traditional electricity consumers into producers, engaging in local energy trading. In the context of distributed energy transactions, blockchain technology has been increasingly applied to facilitate transaction transparency and reliability. However, due to the challenges in collecting accurate energy transmission data from power lines, most existing studies on the blockchain-based transactive energy market are still vulnerable to security attacks, such as malicious users misreporting energy prices, refusing to pay or refusing to transmit energy. Therefore, based on the co-simulation platform PEMT-CoSim and a blockchain, we establish a blockchain-based, reputation-aware secure transactive energy market (STEM) by introducing a reputation scheme to evaluate the trustworthiness of all prosumers and designing reputation-aware, multi-round double auction and energy transmission algorithms to detect and penalize malicious attacks. Furthermore, we run comprehensive experiments for different use cases. The results show that even with malicious participants, the proposed system can guarantee the interests of the honest participants and improve the robustness and effectiveness of the energy market. Full article

(This article belongs to the Special Issue New Applications of Blockchain)

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21 pages, 846 KiB

Open AccessArticle

Decision Tree-Based Federated Learning: A Survey

by Zijun Wang and Keke Gai

Blockchains 2024, 2(1), 40-60; https://doi.org/10.3390/blockchains2010003 - 7 Mar 2024

Cited by 4 | Viewed by 2354

Abstract

Federated learning (FL) has garnered significant attention as a novel machine learning technique that enables collaborative training among multiple parties without exposing raw local data. In comparison to traditional neural networks or linear models, decision tree models offer higher simplicity and interpretability. The integration of FL technology with decision tree models holds immense potential for performance enhancement and privacy improvement. One current challenge is to identify methods for training and prediction of decision tree models in the FL environment. This survey addresses this issue and examines recent efforts to integrate federated learning and decision tree technologies. We review research outcomes achieved in federated decision trees and emphasize that data security and communication efficiency are crucial focal points for FL. The survey discusses key findings related to data privacy and security issues, as well as communication efficiency problems in federated decision tree models. The primary research outcomes of this paper aim to provide theoretical support for the engineering of federated learning with decision trees as the underlying training model. Full article

(This article belongs to the Special Issue Feature Papers in Blockchains)

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20 pages, 2339 KiB

Open AccessArticle

Zk-SNARKs-Based Anonymous Payment Channel in Blockchain

by Yunwei Guo, Haochen Liang, Liehuang Zhu and Keke Gai

Blockchains 2024, 2(1), 20-39; https://doi.org/10.3390/blockchains2010002 - 5 Feb 2024

Cited by 1 | Viewed by 1438

Abstract

Payment channels serve as an effective solution to the scalability problem of cryptocurrencies, which significantly increase transaction rates by allowing users to conduct large-scale offline transactions off-chain without posting everything to the blockchain. However, the existing payment channels lack privacy protection for the transaction amount and the linking relationship between the two parties to the transaction. Therefore, in order to address the scalability and privacy issues of cryptocurrencies such as Bitcoin, this paper proposes a zk-SNARKs-based anonymous payment channel (zk-APC), which supports an unlimited number of off-chain payments between the payer and the payee and protects the privacy of the participants. Specifically, the proposed scheme achieves relational anonymity and amount privacy for both on-chain and off-chain transactions in the payment channel through utilizing zero-knowledge proof (zk-SNARKs) and commitment schemes. This paper proves that the proposed method is more effective than similar schemes through a performance evaluation. Full article

(This article belongs to the Special Issue Feature Papers in Blockchains)

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19 pages, 567 KiB

Open AccessArticle

Decentralization Is Good or Not? Defending Consensus in Ethereum 2.0

by Vojislav B. Mišić, Soosan Naderi Mighan, Jelena Mišić and Xiaolin Chang

Blockchains 2024, 2(1), 1-19; https://doi.org/10.3390/blockchains2010001 - 23 Jan 2024

Cited by 2 | Viewed by 1668

Abstract

Proof-of-Stake (PoS) protocols are widely accepted as a viable substitute for the Proof-of-Work-based consensus, which is why recent blockchain-based cryptocurrencies and applications, most notably Ethereum 2.0, are using some variant of PoS as the basis for the consensus protocol. However, the implementation of PoS protocols in Ethereum 2.0 are not without its share of problems and vulnerabilities, especially with respect to the malicious behavior of validator nodes. In this paper, we first review the basic tenets of PoS protocols. We then discuss some of the recently described attacks on the Ethereum 2.0 consensus, and we also show that some of the design rationales adopted in PoS implementation—the decentralization of the voting process in particular—have, in actuality, enabled attacks that can be launched at a very low cost to the attacker. We also propose simple remedies that can reduce or eliminate the impact of those attacks and can evaluate the performance of the Ethereum 2.0 consensus when these remedies are applied. Full article

(This article belongs to the Special Issue Feature Papers in Blockchains)

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Blockchains, Volume 2, Issue 1 (March 2024) – 4 articles

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