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In recent years, the companies which introduce Bring Your Own Device (BYOD) which utilizes a personal smart phone and tablet for business are increasing in number. However, there are risks, such as information leakage of business information, an employee's personal information, etc., for the private terminal utilization instead of business use. These risks were exhaustively identified in our previous study, but based on qualitative assessment results. In order to make risk countermeasures more realistic, further quantitative evaluation is needed. Therefore, in this paper, we have added new cost risk factors for BYOD from a practical viewpoint to the risk analysis results of previous study. Furthermore, based on the results, a quantitative evaluation was conducted to verify its effectiveness. For the evaluation, the risk factor values were estimated using a risk calculation formula used in the field of information security management systems (ISMS). Thus, the combined effect of the BYOD risk measures proposed in the previous study and the cost risk measures added in this study clarified that it was possible to reduce the risk by about 56%. The results of this quantitative risk assessment are expected to help make the future use of BYOD safer and secure for companies.

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In the era of Digital Transformation (DX), as the Internet continues to become more and more widespread, various devices are now connected to it and the number of IoT devices is increasing. Data generated by IoT devices has traditionally been aggregated in the cloud and processed over time. However, there are two issues with using the cloud. The first is the response delay caused by the long distance between the IoT device and the cloud, and the second is the difficulty of implementing sufficient security measures on the IoT device side due to the limited resources of the IoT device. To address these issues, fog computing, which is positioned in the middle between IoT devices and the cloud, has been attracting attention as a new network component. However, the risks associated with the introduction of fog computing have not yet been fully investigated. In this study, we conducted a risk assessment of fog computing, which is newly established to promote the use of IoT devices, and identified 24 risk factors. The main countermeasures include the gradual introduction of connected IoT connection protocols and security policy matching. We also demonstrated the effectiveness of the proposed risk countermeasures by evaluating the risk values. Furthermore, from a practical viewpoint, the portfolio for the proposed risk countermeasures is mentioned to ensure a more practical risk assessment result. As a result, the proposed risk countermeasures for fog computing will contribute to the safe and secure use of IoT devices.