• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.2
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• pp.33-43
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• 2006

In order to provide the efficient personalized services, the organizations and the companies collect and manage the personal information. However, there have been increasing privacy concerns since the personal information might be misused and spread over in public by the database administrators or the information users. Even in the systems in which organizations or companies control access to personal information according to their access policy in order to protect personal information, it is not easy to fully reflect the information subjects' intention on the access control to their own Personal information. This paper proposes a policy-based access control mechanism for the personal information which prevents unauthorized information users from illegally accessing the personal information and enables the information subjects to control access over their own information. In the proposed mechanism, the individuals' personal information which is encrypted with different keys is stored into the directory repository. For the access control, information subjects set up their own access control policy for their personal information and the policies are used to provide legal information users with the access keys.

• Journal of Platform Technology
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• v.8 no.3
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• pp.22-29
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• 2020

The 4th industrial revolution is progressing by country as a mega trend that leads various technological convergence directions in the social and economic fields from the initial simple manufacturing innovation. The epidemic of infectious diseases such as COVID-19 is shifting digital-centered non-face-to-face business from economic operation, and the use of AI and big data technology for personalized services is essential to spread online. In this paper, we analyze cases focusing on the application of artificial intelligence technology, which is a key technology for the effective implementation of the digital new deal promoted by the government, as well as the major technological characteristics of the 4th industrial revolution and describe the use cases in the field of disaster response. As a disaster response use case, AI assistants suggest appropriate countermeasures according to the status of the reporter in an emergency call. To this end, AI assistants provide speech recognition data-based analysis and disaster classification of converted text for adaptive response.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.115-124
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• 2021

The outbreak of covid-19 has brought the characteristics and advantages of non-contact services to increased prominence, and the development of smart hotels"has accelerated. This study aims to identify, categorize and define the smart service experience at different touch points of the customer experience. The concept and characteristics of the smart hotel were examined based on existing research and literature. An analytical framework was designed using smart experience factors and customer touch points of smart hotels. Selected Chinese smart hotels were then examined under this framework. The case analysis results show that the customer experience design of smart hotels has developed to different degrees, in terms of interactivity, personalization, accessibility, information and privacy security. Based on the above findings, this article suggests that the design of smart hotels should use integrated data to further enhance personalized service experience.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1224-1230
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• 2020

Since the spread of smart phones, interest in wearable devices has increased and diversified, and is closely related to the lives of users, and has been used as a method for providing personalized services. In this paper, we propose a method to detect the user's behavior by applying information from a 3-axis acceleration sensor and a 3-axis gyro sensor embedded in a smartphone to a convolutional neural network. Human behavior differs according to the size and range of motion, starting and ending time, including the duration of the signal data constituting the motion. Therefore, there is a performance problem for accuracy when applied to a convolutional neural network as it is. Therefore, we proposed a Time-Division Feature Fusion Convolutional Neural Network (TDFFCNN) that learns the characteristics of the sensor data segmented over time. The proposed method outperformed other classifiers such as SVM, IBk, convolutional neural network, and long-term memory circulatory neural network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1565-1570
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• 2019

Many possibilities of Big Data has been discussed widely for several years. And the importance of protecting personal information has been emphasized more strongly. During the process of integrating several personal information for the improvement of usability of Big Data, there are many problems occured like the likelihood of the identification of one person, the level of personal infomation used to create personalized services in the companies making and using Big Data. In this study, I summarize GDPR(General Data Protection Regulation) of EU, CCPA(California Consumer Privacy Act) of USA and domestic Big Data 3 Acts Amendment proposals. Also I discuss re-identifcation of de-identificated information, social costs of the usage agreement of personal information, possible problems in construction and combination of private and public big data, political suggestions about settlement of regulatory environment.

• Journal of Dental Rehabilitation and Applied Science
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• v.36 no.4
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• pp.203-210
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• 2020

By facing the era of the 4th industrial revolution, personalized medical services for patients are expanding with the development of information and communications technology. With these changes, digital medical devices have begun to be used to support diagnosis, patient monitoring, and decision-making of diseases, and recently software medical devices for the purpose of preventing, managing, or treating disorders or diseases have become popular. The aim of this article is to understand the current concept and status of Software as a Medical Device (SaMD), which are actively being carried out in the United States, and to find out what fields can be applied in the future. In addition, it intends to find out the Korean domestic policy trends related to smart healthcare and find out the application of digital software as a medical devices that can be used in dental clinic to keep pace with the upcoming changes in the medical field.

• Fashion & Textile Research Journal
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• v.24 no.3
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• pp.279-288
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• 2022

This study aims to derive the need for middle-aged women to consider using fashion product applications, styling, and personalized styling services. To analyze the fashion styling considerations of middle-aged women, 200 women in their 40s and 50s were surveyed. Middle-aged women usually tend to shop through home shopping, department stores, fashion soho (Small office home office) malls, and open market-type applications, and purchase fashion products more than two or three times a month, spending an average of less than 50,000 won per month. Middle-aged women consider choosing appropriate clothing based on the occasion and place, complementing the flaws of the changed body type as well as taking into account the weather in the styling process, and seek to showcase a sophisticated, luxurious, and youthful image through styling. However, they are confused and face difficulties in fashion styling, with regard to not only overall body shape but also partial body changes, such as increasing waistline, flabby thighs and arms, and decreasing hip volume. In addition, middle-aged women were looking for expert advice on styling to help them look the best. They also wanted to solve the difficulties of making a right choice amid the overflowing information related to fashion. The results of the study contribute to identifying products that meet the needs of middle-aged women and help develop detailed consumer-tailored marketing strategies, thereby improving sales of fashion products.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.11-19
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• 2023

The paper proposes a smart mirror system that recommends fragrances based on user emotion analysis. This paper combines natural language processing techniques such as embedding techniques (CounterVectorizer and TF-IDF) and machine learning classification models (DecisionTree, SVM, RandomForest, SGD Classifier) to build a model and compares the results. After the comparison, the paper constructs a personal emotion-based fragrance recommendation mirror model based on the SVM and word embedding pipeline-based emotion classifier model with the highest performance. The proposed system implements a personalized fragrance recommendation mirror based on emotion analysis, providing web services using the Flask web framework. This paper uses the Google Speech Cloud API to recognize users' voices and use speech-to-text (STT) to convert voice-transcribed text data. The proposed system provides users with information about weather, humidity, location, quotes, time, and schedule management.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.223-228
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• 2023

According to the Korea Information Society Development Institute, OTT services grew at a rate of 33.4% in four yearsfrom 2017, when they were first launched.TheKorea Export-Import Bank announced in 2020 that the domestic OTT market was worth 780.1 billionKRW. This growth of the OTT market is expected to stimulate competition among OTT service platforms, and user satisfactionwithconvenience features, such as video recommendations, seems to be acting as an important factor in the competition.Currently, the OTT market uses a variety ofdata for customized recommendations, but the limitationis that it only uses datacollected within the app. Thereby we have proposed the use ofpersonal data collected outside the app for personalized recommendations, and the survey results showed that user satisfaction was 23.72% higher for recommended content based on the proposedmethod thanNetflix recommended content.