• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.641-649
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• 2024

This paper addresses the issue of anonymity protection in the trading of Real-World Asset (RWA) tokens, a prominent topic in the cryptocurrency market in recent years. The principle of transparency inherent in blockchain technology makes it challenging to ensure the anonymity of traders. Although there have been instances in existing blockchain research where mixer services have been utilized to protect the privacy of Fungible Tokens (FTs), and prior studies have explored the privacy protection for Non-Fungible Tokens (NFTs), RWA tokens, which can embody characteristics of both FTs and NFTs and are tied to physical assets, present a complex challenge in achieving the goal of anonymity protection through any single method. This paper proposes a hypothetical token trading platform, ARTeX, and describes the trading process to analyze measures for protecting the anonymity of RWA token transactions.

• Journal of Korean Society for Quality Management
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• v.52 no.3
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• pp.429-457
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• 2024

Purpose: This study aims to provide useful information to real estate investors by developing a profit determination model using artificial intelligence. The model analyzes the real estate markets of six selected cities from multiple perspectives, incorporating characteristics of the real estate market, economic indicators, and policies to determine potential profits. Methods: Data on real estate markets, economic indicators, and policies for five cities were collected and cleaned. The data was then normalized and split into training and testing sets. An AI model was developed using machine learning algorithms and trained with this data. The model was applied to the six cities, and its accuracy was evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared by comparing predicted profits to actual outcomes. Results: The profit determination model was successfully applied to the real estate markets of six cities, showing high accuracy and predictability in profit forecasts. The study provided valuable insights for real estate investors, demonstrating the model's utility for informed investment decisions. Conclusion: The study identified areas for future improvement, suggesting the integration of diverse data sources and advanced machine learning techniques to enhance predictive capabilities.