COVID-19 Case Tracking System in Quarantine Policy: Focus on the Privacy Shift Concept and Application in South Korea.

This study analyzed how Korea's quarantine policy manages personal information to prevent and control COVID-19. Korea effectively halted the spread of COVID-19 through epidemiological investigations and cell-broadcast systems. In this process, the route of infection is presented without identifying the patient, and the necessary participants are selected only through authentication. We found a correlation between the number of emergency text messages sent by the Ministry of Interior and Safety in 2020 and the number of confirmed cases (R2 = 0.465, p < 0.001). Based on Korea's case, we propose a new concept for solving the personal information problems that might arise during a pandemic response.

Detecting Anomalous Transactions via an IoT Based Application: A Machine Learning Approach for Horse Racing Betting.

During the past decade, the technological advancement have allowed the gambling industry worldwide to deploy various platforms such as the web and mobile applications. Government agencies and local authorities have placed strict regulations regarding the location and amount allowed for gambling. These efforts are made to prevent gambling addictions and monitor fraudulent activities. The revenue earned from gambling provides a considerable amount of tax revenue. The inception of internet gambling have allowed professional gamblers to par take in unlawful acts. However, the lack of studies on the technical inspections and systems to prohibit unlawful internet gambling has caused incidents such as the Walkerhill Hotel incident in 2016, where fraudsters placed bets abnormally by modifying an Internet of Things (IoT)-based application called "MyCard". This paper investigates the logic used by smartphone IoT applications to validate the location of users and then confirm continuous threats. Hence, our research analyzed transactions made on applications that operated using location authentication through IoT devices. Drawing on gambling transaction data from the Korea Racing Authority, this research used time series machine learning algorithms to identify anomalous activities and transactions. In our research, we propose a method to detect and prevent these anomalies by conducting a comparative analysis of the results of existing anomaly detection techniques and novel techniques.

A Multi-Component Analysis of CPTED in the Cyberspace Domain.

The visual fidelity of a virtual environment lacks the exceedingly complex layers from the physical world, but the continuous improvements of image rendering technology and computation powers have led to greater demands for virtual simulations. Our study employs Crime Prevention through Environmental Design (CPTED) as a risk control measure and utilizes two principles: Access Control and Natural Surveillance. We conducted an experiment with (n-sample: 100) graduate students. For the experiment, we utilized the Factor Analysis of Information Risk (FAIR) to quantitatively analyze the risk. Furthermore, we adopted the lme4 package for R to estimate the mixed effect of the 6,242,880 observations retrieved from Kaggle. Based on the two experiments, we were able to critically evaluate the contributions of CPTED through a multi-component analysis. Our study investigates how spatial syntax and territorial demarcation may translate in the cyberspace realm. We found that the corollaries of the mophology in the virtual environment effects the distribution of crime. The results of our study discusses how to determine the criminogenic designs and capacity in the cyberspace realm.