As a service platform, blockchain has faced compliance issues since the General Data Protection Regulation (GDPR) came into effect in May 2018. Although many technical solutions have been proposed to solve the compatibility issues between blockchain and the GDPR, unresolved challenges remain. This study presents the gaps between the blockchain and the GDPR and explores solutions to bridge the gap.We review 91 previously published articles using a systematic literature review methodology. Then, we answer the following research questions: 1) Which solutions have been explored to allow the blockchain to comply with the GDPR? 2) What are the research gaps in the blockchain compliance field? Finally, we present five research gaps in this field: 1) development of a consent ontology model; 2) development of a methodology for monitoring fairness in the blockchain; 3) resolution of the contradiction between auditing and obfuscation; 4) development of a methodology for tracking controllers in the blockchain; and 5) integration of the different-purposed technical solutions without conflicts. Our research can raise the compatibility level of the blockchain and GDPR and guide the company adopting a blockchain to comply with the GDPR. Furthermore, it can advise the regulator to embrace new technologies into the GDPR while protecting a blockchain’s nature.

As the metaverse gains traction, the importance of metaverse security research becomes increasingly evident. While there has been research on authenticating users in the metaverse, there is a notable gap in research concerning the authentication of specific spaces within the metaverse. This paper addresses this gap by proposing a novel user-centric blockchain-based authentication approach that incorporates space authentication. The proposed approach leverages blockchain smart contracts to authenticate users using cosine similarity metrics. A significant advantage of this approach its ability to establish user-centric authentication by seamlessly integrating metaverse and blockchain technologies, all without the need for a centralized authority. In this paper, we not only evaluate the security of our proposed approach but also conduct experiments to determine the cosine similarity threshold and assess its feasibility within a metaverse environment.

Recently, organizations face various business challenges because of rapidly changing user needs and expectations. SOA (Service-Oriented Architecture) is a promising technique for adequately handling them in organizations. In developing SOA based systems, service identification is one of the core activities, having a broad influence on the systems. To identify proper services, business goals and business change factors should be analyzed because the ultimate aim of SOA is to achieve business goals and business agility in turbulent business environment. To tackle this, we propose a service identification method based on goal-scenario modeling and a conceptual framework to elicit possible business changes. Traceability among business goals, business changes and identified services are also constructed in this approach. We applied our approach into the HRS (Hotel Reservation System) domain to demonstrate its feasibility.

Among various consensus algorithms, the Byzantine Fault Tolerance (BFT)-based consensus algorithms are broadly used for private blockchain. However, as BFT-based consensus algorithms are structured for all participants to take part in a consensus process, a scalability issue becomes more noticeable. In this approach, we introduce a consensus coordinator to execute a conditionally BFT-based consensus algorithm by classifying transactions. Transactions are divided into equal and unequal transactions. Moreover, unequal transactions are divided again and classified as common and trouble transactions. After that, a consensus algorithm is only executed for trouble transactions, and BFT-based consensus algorithms can achieve scalability. For evaluating our approach, we carried out three experiments in response to three research questions. By applying our approach to PBFT, we obtained 4.75 times better performance than using only PBFT. In the other experiment, we applied our approach to IBFT of Hyperledger Besu, and our result shows a 61.81% performance improvement. In all experiments depending on the change of the number of blockchain nodes, we obtained the better performance than original BFT-based consensus algorithms; thus, we can conclude that our approach improved the scalability of original BFT-based consensus algorithms. We also showed a correlation between performance and trouble transactions associated with transaction issue intervals and the number of blockchain nodes.