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Research on WGAN models with Rényi Differential Privacy
Sujin Lee, Cheolhee Park, Dowon Hong, Jae-kum Kim
http://doi.org/10.5626/JOK.2021.48.1.128
Personal data is collected through various services and managers extract values from the collected data and provide individually customized services by analyzing the results. However, data that contains sensitive information, such as medical data, must be protected from privacy breaches. Accordingly, to mitigate privacy invasion, Generative Adversarial Network(GAN) is widely used as a model for generating synthetic data. Still, privacy vulnerabilities exist because GAN models can learn not only the characteristics of the original data but also the sensitive information contained in the original data. Hence, many studies have been conducted to protect the privacy of GAN models. In particular, research has been actively conducted in the field of differential privacy, which is a strict privacy notion. But it is insufficient to apply it to real environments in terms of the usefulness of the data. In this paper, we studied GAN models with Rényi differential privacy, which preserve the utility of the original data while ensuring privacy protection. Specifically, we focused on WGAN and WGAN-GP models, compared synthetic data generated from non-private and differentially private models, and analyzed data utility in each scenario.
Learning Semantic Features for Dense Video Captioning
http://doi.org/10.5626/JOK.2019.46.8.753
In this paper, we propose a new deep neural network model for dense video captioning. Dense video captioning is an emerging task that aims at both localizing and describing all events in a video. Unlike many existing models, which use only visual features extracted from the given video through a sort of convolutional neural network(CNN), our proposed model makes additional use of high-level semantic features that describe important event components such as actions, people, objects, and backgrounds. The proposed model localizes temporal regions of events by using LSTM, a recurrent neural network(RNN). Furthermore, our model adopts an attention mechanism for caption generation to selectively focus on input features depending on their importance. By conducting experiments using a large-scale benchmark dataset for dense video captioning, AcitivityNet Captions, we demonstrate high performance and superiority of our model.
Research for Speed Improvement Method of Lightweight Block Cipher CHAM using NEON SIMD
Sujin Lee, Junyoung Kang, Dowon Hong, Changho Seo
http://doi.org/10.5626/JOK.2019.46.5.485
As embedded devices and IoT devices are being developed, lightweight block ciphers have been proposed to achieve confidentiality on low-end devices. Recently, a lightweight block cipher algorithm, called CHAM, with 4-branch Feistel structure was proposed in Korea. It is consists of CHAM-64/128, CHAM-128/128, and CHAM-128/256 depending on the size of plaintext and secret key. CHAM, which is based on ‘stateless on the fly’ key schedule and ARX operations, is efficient on embedded devices, especially on low-end devices. In this paper, we analyze the lightweight block cipher CHAM and study an optimization method on a high-end IoT device. We implemented a serial code by independently generating round keys and leveraging 4-branch Feistel structure, and optimized CHAM using NEON-SIMD on ARM Cortex-A53.
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