• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1470-1476
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• 2020

This paper describes a design of a lightweight security system-on-chip (SoC) suitable for the implementation of security protocols for IoT and mobile devices. The security SoC using Cortex-M0 as a CPU integrates hardware crypto engines including an elliptic curve cryptography (ECC) core, a SHA3 hash core, an ARIA-AES block cipher core and a true random number generator (TRNG) core. The ECC core was designed to support twenty elliptic curves over both prime field and binary field defined in the SEC2, and was based on a word-based Montgomery multiplier in which the partial product generations/additions and modular reductions are processed in a sub-pipelining manner. The H/W-S/W co-operation for elliptic curve digital signature algorithm (EC-DSA) protocol was demonstrated by implementing the security SoC on a Cyclone-5 FPGA device. The security SoC, synthesized with a 65-nm CMOS cell library, occupies 193,312 gate equivalents (GEs) and 84 kbytes of RAM.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.163-170
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• 2022

LEA is a lightweight block cipher developed in Korea in 2013. In this paper, among block cipher operation methods, CTR operation mode and GCM operation mode that provides confidentiality and integrity are implemented. In the LEA-CTR operation mode, we propose an optimization implementation that omits the operation between states through the state fixation and omits the operation through the pre-operation by utilizing the characteristics of the fixed nonce value of the CTR operation mode. It also shows that the proposed method is applicable to the GCM operation mode, and implements the GCM through the implementation of the GHASH function using the Galois Field(2¹²⁸) multiplication operation. As a result, in the case of LEA-CTR to which the proposed technique is applied on 32-bit RISC-V, it was confirmed that the performance was improved by 2% compared to the previous study. In addition, the performance of the GCM operation mode is presented so that it can be used as a performance indicator in other studies in the future.

• Journal of Convergence for Information Technology
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• v.8 no.3
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• pp.79-84
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• 2018

Cloud services are readily available through a variety of media, attracting a lot of attention from users. However, there are various security damages that abuse the privacy of users who use cloud services, so there is not enough technology to prevent them. In this paper, we propose a protection model to safeguard user's privacy in a cloud environment so as not to illegally exploit user's privacy. The proposed model randomly manages the user's signature to strengthen the role of the middle manager and the cloud server. In the proposed model, the user's privacy information is provided illegally by the cloud server to the user through the security function and the user signature. Also, the signature of the user can be safely used by bundling the random number of the multiplication group and the one-way hash function into the hash chain to protect the user's privacy. As a result of the performance evaluation, the proposed model achieved an average improvement of data processing time of 24.5% compared to the existing model and the efficiency of the proposed model was improved by 13.7% than the existing model because the user's privacy information was group managed.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.441-446
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• 2020

In this paper, we propose the α-Feature Map Scaling (α-FMS) method which extends the FMS method that was designed to enhance the discriminative power of feature maps of deep neural networks in Speaker Verification (SV) systems. The FMS derives a scale vector from a feature map and then adds or multiplies them to the features, or sequentially apply both operations. However, the FMS method not only uses an identical scale vector for both addition and multiplication, but also has a limitation that it can only add a value between zero and one in case of addition. In this study, to overcome these limitations, we propose α-FMS to add a trainable parameter α to the feature map element-wise, and then multiply a scale vector. We compare the performance of the two methods: the one where α is a scalar, and the other where it is a vector. Both α-FMS methods are applied after each residual block of the deep neural network. The proposed system using the α-FMS methods are trained using the RawNet2 and tested using the VoxCeleb1 evaluation set. The result demonstrates an equal error rate of 2.47 % and 2.31 % for the two α-FMS methods respectively.