A blockchain is a distributed ledger that allows users to exchange information without a centralized authority. This technology enables users to send and receive tokens among other applications, such as transactions, product management, and elections. It is possible to send data and tokens inside a single blockchain, but a method to efficiently share the data and tokens among different blockchains has not yet been constructed. Cross-chain communication, the focal point of several recent research efforts, is a scheme for sending data or tokens among different blockchains. In existing studies, a trusted third party (TTP) is used to ensure fair rates of token exchange among different blockchains. However, because blockchains are originally designed with a policy that does not incorporate the use of TTPs, the fair exchange rate should not be determined by TTPs, but rather by the market price of tokens among users. When exchange rates are determined from quotes among users, the preferred scheme is to determine the exchange rate offered by many users as an auction. Here, some existing cross-chain communication systems use smart contracts that automatically execute arbitrary processes on the blockchain. However, such schemes require a gas fee each time a smart contract is executed. Thus, implementing an auction scheme that determines the fair exchange rate among different blockchains would necessitate each user to pay a fee for each new token offered, which would result in high gas fees. In this study, we propose a scheme to determine exchange rates from quotes among users with a relatively low gas fee. Using a first-price sealed-bid auction and commitment scheme, the user with the highest token value can be identified without revealing the other users’ token offer values. In our scheme, the largest token value among users is determined as the exchange rate using an external Smart Contract (SC) instead of a TTP. We further modify the existing insert key-value commitment scheme to aggregate the commitment values of token offers. Our scheme is based on the generalized RSA assumption. By proving that it satisfies the key-binding property, we prove that the token sender cannot act maliciously. We further implement the proposed scheme and demonstrate that the gas fees and data space required to implement the proposed scheme are practically feasible. Copyright © 2026 The Institute of Electronics, Information and Communication Engineers.

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 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.