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Blockchains, Volume 3, Issue 1 (March 2025) – 2 articles

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23 pages, 613 KiB  
Article
PROACTION: Profitable Transactions Selection Greedy Algorithm in Rational Proof-of-Work Mining
by Mariano Basile, Giovanni Nardini, Pericle Perazzo and Gianluca Dini
Blockchains 2025, 3(1), 2; https://doi.org/10.3390/blockchains3010002 - 22 Jan 2025
Viewed by 419
Abstract
Despite the many consensus algorithms being used in blockchains, proof of work (PoW) is still the most common nowadays. The state-of-the-art mining strategy for PoW-based blockchain protocols consists of including as many transactions as possible in a block to maximize the block reward. [...] Read more.
Despite the many consensus algorithms being used in blockchains, proof of work (PoW) is still the most common nowadays. The state-of-the-art mining strategy for PoW-based blockchain protocols consists of including as many transactions as possible in a block to maximize the block reward. Unfortunately, this strategy maximizes the block orphaning probability too. Recently, we proposed a rational mining strategy aimed at carefully balancing the trade-off between the block reward and the risk of block orphaning. In this work, we present PROACTION, a PROfitable transACTions selectION greedy algorithm that implements such a strategy. We evaluate the algorithm both analytically and experimentally on Bitcoin by assuming a variable random percentage of winning miners adopting PROACTION. Experiments show that when executing PROACTION, miners gain higher long-term rewards than when using the state-of-the-art strategy. The gain is in the order of the block orphaning probability. This result is particularly relevant for those PoW-based blockchain protocols in which such a probability is significant. Full article
(This article belongs to the Special Issue Feature Papers in Blockchains)
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38 pages, 1964 KiB  
Review
Blockchain-Based Privacy-Enhancing Federated Learning in Smart Healthcare: A Survey
by Zounkaraneni Ngoupayou Limbepe, Keke Gai and Jing Yu
Blockchains 2025, 3(1), 1; https://doi.org/10.3390/blockchains3010001 - 1 Jan 2025
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Abstract
Federated learning (FL) has emerged as an efficient machine learning (ML) method with crucial privacy protection features. It is adapted for training models in Internet of Things (IoT)-related domains, including smart healthcare systems (SHSs), where the introduction of IoT devices and technologies can [...] Read more.
Federated learning (FL) has emerged as an efficient machine learning (ML) method with crucial privacy protection features. It is adapted for training models in Internet of Things (IoT)-related domains, including smart healthcare systems (SHSs), where the introduction of IoT devices and technologies can arise various security and privacy concerns. However, as FL cannot solely address all privacy challenges, privacy-enhancing technologies (PETs) and blockchain are often integrated to enhance privacy protection in FL frameworks within SHSs. The critical questions remain regarding how these technologies are integrated with FL and how they contribute to enhancing privacy protection in SHSs. This survey addresses these questions by investigating the recent advancements on the combination of FL with PETs and blockchain for privacy protection in smart healthcare. First, this survey emphasizes the critical integration of PETs into the FL context. Second, to address the challenge of integrating blockchain into FL, it examines three main technical dimensions such as blockchain-enabled model storage, blockchain-enabled aggregation, and blockchain-enabled gradient upload within FL frameworks. This survey further explores how these technologies collectively ensure the integrity and confidentiality of healthcare data, highlighting their significance in building a trustworthy SHS that safeguards sensitive patient information. Full article
(This article belongs to the Special Issue Feature Papers in Blockchains)
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