The use of big data in various fields has led to a rapid increase in a wide variety of data resources, and various data analysis technologies such as standardized data mining and statistical analysis techniques are accelerating the continuous expansion of the big data market. An important characteristic of big data is that data from various sources have life cycles from collection to destruction, and new information can be derived through analysis, combination, and utilization. However, each phase of the life cycle presents data security and reliability issues, making the protection of personally identifiable information a critical objective. In particular, user tendencies can be analyzed using various big data analytics, and this information leads to the invasion of personal privacy. Therefore, this paper identifies threats and security issues that occur in the life cycle of big data by confirming the current standards developed by international standardization organizations and analyzing related studies. In addition, we divide a big data life cycle into five phases (i.e., collection, storage, analytics, utilization, and destruction), and define the security taxonomy of the big data life cycle based on the identified threats and security issues.

Background: With the recent use of IT in health care, a variety of eHealth data are increasingly being collected and stored by national health agencies. As these eHealth data can advance the modern health care system and make it smarter, many researchers want to use these data in their studies. However, using eHealth data brings about privacy and security concerns. The analytical environment that supports health care research must also consider many requirements. For these reasons, countries generally provide research platforms for health care, but some data providers (eg, patients) are still concerned about the security and privacy of their eHealth data. Thus, a more secure platform for health care research that guarantees the utility of eHealth data while focusing on its security and privacy is needed. Objective: This study aims to implement a research platform for health care called the health care big data platform (HBDP), which is more secure than previous health care research platforms. The HBDP uses attribute-based encryption to achieve fine-grained access control and encryption of stored eHealth data in an open environment. Moreover, in the HBDP, platform administrators can perform the appropriate follow-up (eg, block illegal users) and monitoring through a private blockchain. In other words, the HBDP supports accountability in access control. Methods: We first identified potential security threats in the health care domain. We then defined the security requirements to minimize the identified threats. In particular, the requirements were defined based on the security solutions used in existing health care research platforms. We then proposed the HBDP, which meets defined security requirements (ie, access control, encryption of stored eHealth data, and accountability). Finally, we implemented the HBDP to prove its feasibility. Results: This study carried out case studies for illegal user detection via the implemented HBDP based on specific scenarios related to the threats. As a result, the platform detected illegal users appropriately via the security agent. Furthermore, in the empirical evaluation of massive data encryption (eg, 100,000 rows with 3 sensitive columns within 46 columns) for column-level encryption, full encryption after column-level encryption, and full decryption including column-level decryption, our approach achieved approximately 3 minutes, 1 minute, and 9 minutes, respectively. In the blockchain, average latencies and throughputs in 1Org with 2Peers reached approximately 18 seconds and 49 transactions per second (TPS) in read mode and approximately 4 seconds and 120 TPS in write mode in 300 TPS. Conclusions: The HBDP enables fine-grained access control and secure storage of eHealth data via attribute-based encryption cryptography. It also provides nonrepudiation and accountability through the blockchain. Therefore, we consider that our proposal provides a sufficiently secure environment for the use of eHealth data in health care research.

The rapid advancement of the industrial Internet of things (IIoTs) has facilitated the development of diverse platforms and management technologies. However, the lack of standardized protocols and interoperability between platforms developed by various organizations hinders seamless integration. These limitations represent the primary challenge in access control between different IIoT platforms, resulting in limited service utilization across heterogeneous platforms. This study addresses the challenges of effectively sharing user attributes and representations for access control between heterogeneous IIoT platforms using blockchain and a metadata registry (MDR). Utilizing blockchain ensures tamper-proof and transparent attribute sharing, whereas the MDR facilitates the accurate mapping of user attributes for cross-platform resource access control. We demonstrated the feasibility of the proposed framework by successfully implementing access control between two representative standard platforms, namely oneM2M and FIWARE. Our results indicate that enhancing service utilization through access control between heterogeneous IIoT platforms can improve security and efficiency across various industries.

As many companies adopted hybrid work arrangements during and after the COVID-19 outbreak, interest in Metaverse applications for virtual offices grew considerably. Along with this growing interest, the risk of data breaches has also increased, as virtual offices often handle confidential documents for businesses. For this reason, existing studies have explored Metaverse user authentication methods; however, their methods suffer from several limitations, such as the need for additional sensors and one-time authentication. Therefore, this paper proposes a novel behavioral authentication framework for secure Metaverse workspaces. The proposed framework adopts keyboard typing behavior that is common in the office and does not cause fatigue to users as an authentication factor to afford active and continuous user authentication. Based on our evaluation, the user identification accuracy achieved an average of approximately 95% among 11 of 15 participants, with the highest-performing user reaching an accuracy of 99.77%. In addition, the proposed framework achieved an average false acceptance rate of 0.41% and a false rejection rate of 4.02%. It was also evaluated with existing studies using requirements for user authentication in the Metaverse to demonstrate its strengths. Therefore, this framework can fully ensure a secure Metaverse office by preventing unauthenticated users.

As IoT technology is applied to various fields, many IoT platforms are being developed to provide connections between numerous services and users. However, this diversity and heterogeneity can limit interoperability. Users on different platforms cannot be authenticated because IoT platforms have different representations and data formats for the same resource and do not understand each other's data models. In addition, users on other platforms cannot grant permissions because they do not understand the policies and required attribute values of other platforms. To solve these problems, this study proposes a framework focusing on access control, such as authentication and authorization for resource use between various IoT platforms.