• Convergence Security Journal
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• v.24 no.1
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• pp.69-76
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• 2024

The white-box implementation is a cryptographic technique used to protect the secret key of a cryptographic system. It is primarily employed for digital rights management for music and videos. Since 2002, numerous white-box implementations have been developed to ensure secure digital rights management. These have been applied to AES and DES. ARIA, a 128-bit block cipher with an involution substitution and permutation network (SPN), was selected as a South Korean standard in 2004. In this paper, we propose the first white-box ARIA implementation. Our implementation consists of 7,696 lookup tables, with a total size of 1,984 KB. We demonstrate that it also has considerable white-box diversity and white-box ambiguity from a security perspective.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.11-19
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• 2020

Modern GPU can execute general purpose computation on the graphic processing unit, and provide high performance by exploiting many core on GPU. To run AES algorithm efficiently, parallel computational resources are required. However, computational resource of CPU architecture are not enough to cryptographic algorithm such as AES whereas GPU architecture has mass parallel computation resources. Therefore, this paper reduce the time to execute AES by employing parallel computational resource on GPGPU. Unfortunately, AES cannot utilize computational resource on GPGPU since it isn't suitable to GPGPU architecture. In this paper, IPC based dynamic SM management technique are proposed to efficiently execute AES on GPGPU. IPC based dynamic SM management can increase and decrease the number of active SMs by using IPC in run-time. According to simulation results, proposed technique improve the performance by increasing resource utilization compared to baseline GPGPU architecture. The results show that AES improve the performance by 41.2% on average.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.43-50
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• 2022

Correlation Power Analysis(CPA) is a sub-channel attack method that measures the detailed power consumption of attack target equipment equipped with cryptographic algorithms and guesses the secret key used in cryptographic algorithms with more than 90% probability. Since CPA performs analysis based on statistics, a large amount of data is necessarily required. Therefore, the CPA must measure power consumption for at least about 15 minutes for each attack. In this paper proposes a method of using a Convolutional Neural Network(CNN) capable of accumulating input data and predicting results to solve the data collection problem of CPA. By collecting and learning the power consumption of the target equipment in advance, entering any power consumption can immediately estimate the secret key, improving the computational speed and 96.7% of the secret key estimation accuracy.