Blockchains, Volume 3, Issue 1 (March 2025) – 5 articles

Blockchain technology has risen in recent years from its initial application in finance to gain prominence across diverse sectors, including digital forensics. The possible application of blockchain technology to digital forensics is now becoming increasingly explored with many researchers now looking into the unique inherent properties that blockchain possesses to address the inherent challenges in this sector such as evidence tampering, the lack of transparency, and inadmissibility in court. Despite the increasing interest in integrating blockchain technology into the field of digital forensics and its domains, no systematic literature review currently exists to provide a holistic perspective on this integration. It is a challenge to find a comprehensive resource that examines how blockchain is being applied to enhance the digital forensics process. This paper provides a systematic literature review to explore the application of blockchain technology in digital forensics, focusing on its potential to address these challenges and enhance forensic methodologies. Through a rigorous review process, this paper examines selected studies to identify diverse frameworks, methodologies, and blockchain-driven enhancements applied to digital forensic investigations. The discussion highlights how blockchain properties such as immutability, transparency, and automation have been leveraged to improve evidence management and forensic workflows. Furthermore, this paper explores the common applications of blockchain-based forensic solutions across various domains and phases while addressing the associated limitations and challenges. Open issues and future research directions, including unexplored domains and operational gaps, are also discussed. This study provides valuable insights for researchers, investigators, and policymakers by offering a comprehensive overview of the state of the art in blockchain-based digital forensics, summarizing key contributions and limitations, and identifying pathways for advancing the field. Full article

Decentralization is a core principle of blockchain technology and Decentralized Autonomous Organizations (DAOs), enhancing security and resilience by distributing control across a network. Traditional metrics like the Gini coefficient and Nakamoto coefficient often fall short in capturing the complex dynamics of decentralization. This paper introduces the Apokedro decentralization index, a metric that evaluates decentralization by considering the probabilities of all possible subsets of nodes that could collectively centralize control. These concepts from game theory, such as the Nash equilibrium, and the Apokedro index, when incorporated, provide a nuanced assessment of centralization risks. Key contributions include the mathematical formulation of the index, an efficient computational algorithm utilizing pruning techniques, and benchmarking experiments that compare the index performance against traditional metrics across various statistical distributions. The Apokedro index offers a comprehensive tool for measuring decentralization in blockchain networks and DAOs. Full article

The education sector has witnessed a significant shift towards digitising student records, with relevant data now stored in centralized data repositories. While traditional identity management solutions in education are functional, they often face various challenges, including data privacy concerns, limited portability, and reliability challenges. As the volume of student data continues to grow, inadequate data management practices have led to several problems. These include students losing control and empowerment over their educational information, increased vulnerability to potential data breaches and unauthorized access, a lack of transparency and accountability, data silos and inconsistencies, and administrative inefficiencies. To address these limitations, the implementation of a blockchain-assisted self-sovereign identity (Ba-SSI) concept in the education system presents a viable solution. Self-sovereign identity (SSI) represents a paradigm shift from traditional centralized identity systems, allowing individuals to maintain full control of their identity data without relying on centralized authorities. By leveraging the decentralized nature, SSI frameworks can ensure security, interoperability, and scalability, thereby improving user-centric identity management. This survey paper explores the potential of Ba-SSI within the context of education. It thoroughly reviews the current state of digital identity management in education, highlighting the limitations of conventional systems and the emerging role of blockchain technology in addressing these challenges. The paper discusses the fundamental principles of blockchain technology and how it can be utilized to enhance security, interoperability, and scalability in identity management. Additionally, it examines the insights and benefits of this approach for the education system. Finally, the paper concludes by addressing the issues, challenges, benefits, and future research directions in this domain, underscoring the potential of Ba-SSI solutions to revolutionize the management and empowerment of student data within the education sector. Full article

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

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