Blockchain that is a decentralized data management platform is attracting much attention recently. Recent advancement of blockchain technology increases the demand and supply of Non-Fungible Tokens (NFTs) that enable management of ownership concepts for various digital contents. Especially in the metaverse, NFT contents such as images and videos are actively used to facilitate communication within the virtual space. However, systems and technologies that link NFT content in the metaverse with metaverse platforms and generate metadata (e.g., popularity of each content) from data created through user's activities in the metaverse (e.g., user attention, conversation audio) are not realized. Therefore, our research aims to provide highly reliable data regarding the popularity and evaluation function of NFT content. To achieve the objective, we focus on extending existing oracle mechanisms and developing a system that integrates NFT content and various data generated on the metaverse platforms. The oracle technology to handle data provision and verification processes allows users within the metaverse to mutually verify reliability of the data generated through interactions with NFT content. Additionally, the proposed platform enables the use of common devices (e.g., smartphones) as wallets for the blockchain. These common devices can be used to sign transactions through short-range wireless communication when sending data to the blockchain through devices constituting the metaverse platform. This approach ensures user's involvement in data transmission procedure while simultaneously enabling mutual verification of the transmitted data through the blockchain to achieve both wallet security and data reliability. © 2025 IEEE.

The widespread adoption of IoT devices has led to increased sensor data. Among the data, we defined spatio-temporal data (STD) as data that depends on both time and space. We proposed a spatio-temporal data retention system (STD-RS) that uses vehicles to retain and utilize STD within a specific spatial area. However, in STD-RS, errors in vehicle location information can cause data to be transmitted outside the target area. Previously, we proposed a method to detect abnormal vehicles by learning the distance and received signal strength indication (RSSI) of the vehicle and then confirmed that the method can detect abnormal vehicles with an accuracy of approximately 80%. However, the previous method did not consider vehicles that cannot generate features for learning in this paper. Moreover, as the evaluation was conducted in a single (ideal) environment only, the validation of effectiveness in practical environments remains. In this study, we use the sufficiency rate of data necessary for feature generation as an evaluation metric and evaluate how the spatial and quantitative changes in the combination of features for learning impact the sufficiency rate and detection accuracy under practical environments. The simulation evaluation results revealed the factors that affected the sufficiency rate and demonstrated that using the long-term spatial variation of RSSI as a feature improves the detection accuracy in any environment. © 2025 IEEE.

Location-Based Recommendation Systems (LBRS) use device location data to suggest nearby hotels, restaurants, and points of interest. Since directly collecting location data from users can raise privacy concerns, there is growing interest in building recommendation systems based on Federated Learning (FL). Under FL, parameters of recommendation model learned on each user’s device are collected on a single server to build aggregated model. While FL does not raise privacy concerns about data collection since it does not collect user data directly, it may construct unfair models that repeatedly recommend specific locations. Although there are training methods to achieve fair recommendations that prevent such bias, they require more training epochs than usual. In FL, a malicious server can infer the original location data by continuously tracking a specific user’s parameter updates, and the inference accuracy increases proportionally with the number of training epochs. This means that achieving fair location recommendations in FL puts the original data at risk. In this paper, we design a novel parameter aggregation method to build fair and secure FL recommendation models. In the proposed aggregation method, users exchange parameters with each other before model aggregation to prevent malicious servers from inferring the original data. Even if a server (adversary) continuously tracks a specific user’s device, it cannot get parameters from the same user, thus preventing inference of the original location data. An experiment result demonstrated that the proposed method can reduce training time while maintaining the same accuracy as homomorphic encryption approach. © 2013 IEEE.

The Spatio-Temporal Data-Retention System (STDRS) disseminates and retains spatio-temporal data (STD) within a specific area using user device mobility and wireless communication. Conventional STD-RS employs broadcasting to uniformly distribute data without queries, which can lead to redundant transmissions and unreliable delivery. To address this, we propose incorporating Information-Centric Networking (ICN) into STDRS, enabling content-based data retrieval that reduces unnecessary dissemination while ensuring access to relevant information. © 2025 IEEE.

The digitization of financial transactions has led to a rise in credit card fraud, necessitating robust measures to secure digital financial systems from fraudsters. Nevertheless, traditional centralized approaches for detecting such frauds, despite their effectiveness, often do not maintain the confidentiality of financial data. Consequently, Federated Learning (FL) has emerged as a promising solution, enabling the secure and private training of models across organizations. However, the practical implementation of FL is challenged by data heterogeneity among institutions, complicating model convergence. To address this issue, we propose FedFusion, which leverages the fusion of local and global models to harness the strengths of both, ensuring convergence even with heterogeneous data with total feature discrepancy. Our approach involves three distinct datasets with completely different feature sets assigned to separate federated clients. Prior to FL training, datasets are preprocessed to select significant features across three deep learning models. The Multilayer Perceptron (MLP), identified as the best-performing model, undergoes personalized training for each dataset. These trained MLP models serve as local models, while the main MLP architecture acts as the global model. FedFusion then adaptively trains all clients, optimizing fusion proportions. Experimental results demonstrate the approach's superiority, achieving detection rates of 99.74%, 99.70%, and 96.61% for clients 1, 2, and 3, respectively. This highlights the effectiveness of FedFusion in addressing data heterogeneity challenges, thereby paving the way for more secure and efficient fraud detection systems in digital finance.