• Proceedings of the KIPE Conference
• /
• 2013.07a
• /
• pp.256-257
• /
• 2013

이 논문에서는 셔플링 컨버터를 이용해 다중 태양광 모듈의 최대 전력점 추종시 각 태양광 모듈간의 전류 편차에 의해 셔플링 컨버터에 과전류가 흐르는 것을 방지하기 위해 태양광 모듈에서의 전류 편차를 이용해 셔플링 컨버터에서의 전류를 계산하는 법을 고안하고, 이를 이용해 전류를 제한하는 기법을 제안하였다. 제안하는 방식을 증명하기 위해 PSIM 소프트웨어를 이용하여 그 타당성을 입증하였다.

• Proceedings of the KIPE Conference
• /
• 2013.11a
• /
• pp.27-28
• /
• 2013

이 논문에서는 부분 그늘짐에서도 다중 태양광 모듈의 최대 전력점 추종이 가능한 셔플링 차동전력 마이크로 컨버터를 설계 및 구현하였다. 마이크로 컨버터 시스템은 PV에 직접 연결된 셔플링 컨버터와 이후 단으로서 부스트 컨버터가 존재한다. 셔플링 컨버터는 다이렉트-시비율 기법을 통하여 PV모듈을 최대전력으로 동작시키고, 부스트 컨버터는 전압루프를 통하여 P&O 방식으로 최대 전력점을 추종하였다. PV 모듈에 병렬로 셔플링 컨버터를 적용함으로써, 다중 태양광 모듈의 전류 편차를 차동전력으로 조절하여 각각의 최대 전력점을 추종하면서 안정된 동작을 유지할 수 있다. 제안하는 컨버터는 2개의 PV모듈이 연결된 정상상태 연속모드 (CCM) 160[W]급 마이크로 컨버터 하드웨어 프로토타입을 통해 동작특성을 분석하였다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.5C
• /
• pp.497-502
• /
• 2006

In this paper, we propose a digital image encryption method using adaptive interleaving and multiple random shuffling table to improve the existing encryption methods which use a fixed random shuffling table. In order to withstand the plaintext attack, at first, we propose a interleaving method that is adaptive to the local feature of image. Secondly, using the proposed interleaving only shuffling method and multiple shuffling method that is combined interleaving with existing random shuffling method, we encrypted image by shuffled the DPCM processed $8^*8$ blocks. Experimental results show that, the proposed algorithm is very robust to plaintext attack and there is no overhead bit.

• Journal of Korea Multimedia Society
• /
• v.10 no.1
• /
• pp.58-65
• /
• 2007

This dissertation proposes digital video protection algorithm lot moving image based on MPEG. Shuffling-based encryption algorithms using a fixed random shuffling table are quite simple and effective but vulnerable to the chosen plaintext attack. To overcome this problem, it is necessary to change the key used for generation of the shuffling table. However, this may pose a significant burden on the security key management system. A better approach is to generate the shuffling table based on the local feature of an image. In order to withstand the chosen plaintext attack, at first, we propose a interleaving algorithm that is adaptive to the local feature of an image. Secondly, using the multiple shuffling method which is combined interleaving with existing random shuffling method, we encrypted the DPCM processed 8*8 blocks. Experimental results showed that the proposed algorithm needs only 10% time of SEED encryption algorithm and moreover there is no overhead bit. In video sequence encryption, multiple random shuffling algorithms are used to encrypt the DC and AC coefficients of intra frame, and motion vector encryption and macroblock shuffling are used to encrypt the intra-coded macroblock in predicted frame.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.49 no.9
• /
• pp.55-64
• /
• 2012

Until now, Side Channel Attack has been known to be effective to crack decrypt key such as smart cards, electronic passports and e-ID card based on Chip. Combination of Masking and shuffling methods have been proposed practical countermeasure. Newly, S.Tillich suggests biased-mask using template attack(TA) to attack AES with masking and shuffling. However, an additional assumption that is acquired template information previously for masking value is necessary in order to apply this method. Moreover, this method needs to know exact time position of the target masking value for higher probability of success. In this paper, we suggest new practical method called Biasing Power Analysis(BPA) to find a secret key of AES based on masking-shuffling method. In BPA, we don't use time position and template information from masking value. Actually, we do experimental works of BPA attack to 128bit secret key of AES based on masking-shuffling method performed MSP430 Chip and we succeed in finding whole secret key. The results of this study will be utilized for next-generation ID cards to verify physical safety.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.15 no.3
• /
• pp.211-216
• /
• 2022

Steganography technology protects the existence of hidden information by embedding a secret message in a specific location on the cover medium. Security and resistance are strengthened by applying various hybrid methods based on encryption and steganography. In particular, techniques to increase chaos and randomness are needed to improve security. In fact, the case where the shuffling method is applied based on the discrete cosine transform(DCT) and the least significant bit(LSB) is an area that needs to be studied. I propose a new approach to hide the bit information of Hangul messages by integrating the selective shuffling method that can add the complexity of message hiding and applying the spatial domain technique to steganography. Inverse shuffling is applied when extracting messages. In this paper, the Hangul message to be inserted is decomposed into the choseong, jungseong and jongseong. It improves security and chaos by applying a selective shuffling process based on the corresponding information. The correlation coefficient and PSNR were used to confirm the performance of the proposed method. It was confirmed that the PSNR value of the proposed method was appropriate when compared with the reference value.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.965-968
• /
• 2012

스마트 디바이스 내에 내재된 암호 알고리즘의 이론적인 안전성이 보장되었더라도 연산 수행 시 소모되는 전력소모, 전자기파와 같은 물리적 정보와의 관계를 분석하는 부채널 공격에 대해 취약하다. 이에 대해 부채널 공격의 대표적 대응 기법으로 셔플링 기법과 마스킹 기법이 제안되었다. 특히 셔플링을 활용한 대응 기법은 효율성을 최대한 유지하면서 분석의 난이도를 높이는 기법으로 잘 알려져 왔다. 본 논문에서는 AES 대칭키 암호의 부채널 대응 기법으로 8가지 AES 구현 기법을 셔플링 하여 구현할 경우 부채널 분석에 미치는 영향에 대해 연구하였다. 실험 결과 실제 기대되어지는 분석 난이도는 셔플링을 하지 않은 것에 비해 64배 정도 공격 복잡도가 높아져야 하지만, 실제는 7배정도의 공격 복잡도 증가로 분석이 되었다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.11
• /
• pp.1507-1518
• /
• 2020

High-quality images have a lot of information, so sensitive data is stored by encryption for private company, military etc. Encrypted images can only be decrypted with a secret key, but the original data cannot be retained when attacked by the Shear attack and Noise pollution attack techniques that overwrite some pixel data with arbitrary values. Important data is the more necessary a countermeasure for the recovery method against attack. In this paper, we propose a random number generator PingPong256 and a shuffling method that rearranges pixels to resist Shear attack and Noise pollution attack techniques so that image and video encryption can be performed more quickly. Next, the proposed PingPong256 was examined with SP800-22, tested for immunity to various noises, and verified whether the image to which the shuffling method was applied satisfies the Anti-shear attack and the Anti-noise pollution attack.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.6
• /
• pp.919-928
• /
• 2023

Deep learning technology is used in various fields such as self-driving cars, image creation, and virtual voice implementation, and deep learning accelerators have been developed for high-speed operation in hardware devices. However, several side channel attacks that recover secret information inside the accelerator using side-channel information generated when the deep learning accelerator operates have been recently researched. In this paper, we implemented a DNN(Deep Neural Network)-based MNIST digit classifier on a microprocessor and attempted a correlation power analysis attack to confirm that the weights of deep learning accelerator could be sufficiently recovered. In addition, to counter these power analysis attacks, we proposed a Node-CUT shuffling method that applies the principle of misalignment at the time of power measurement. It was confirmed through experiments that the proposed countermeasure can effectively defend against side-channel attacks, and that the additional calculation amount is reduced by more than 1/3 compared to using the Fisher-Yates shuffling method.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.2
• /
• pp.299-304
• /
• 2023

Among artificial intelligence techniques, deep learning is a model that has been used in many places and has proven its effectiveness. However, deep learning models are not used effectively in everywhere. In this paper, we will show the limitations of deep learning models through comparison of regression analysis and deep learning models, and present a guide for effective use of deep learning models. In addition, among various techniques used for optimization of deep learning models, data normalization and data shuffling techniques, which are widely used, are compared and evaluated based on actual data to provide guidelines for increasing the accuracy and value of deep learning models.