• 정보보호학회논문지
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• 제29권3호
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• pp.565-577
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• 2019

최근에 생체인증은 다양한 분야에 사용되고 있다. 생체정보는 변경이 불가능하고 다른 개인정보와 달리 폐기할 수 없기 때문에 생체정보 유출에 대한 우려가 커지고 있다. 최근 Jin et al.은 지문 템플릿을 보호하기 위해 IoM(Index-of-Max) 해싱이라는 폐기가능한 생체인증 방법을 제안했다. Jin et al.은 Gaussian random projection 기반과 Uniformly random permutation 기반의 두 가지 방법을 구현하였다. 제안된 방법은 높은 매칭 정확도를 제공하고 프라이버시 공격에 강력함을 보여주며 폐기가능한 생체인증의 요건을 만족함을 보여주었다. 그러나 Jin et al.은 다른 생체정보에 대한 인증(예: 정맥, 홍채 등)에 대한 실험 결과를 제공하지는 않았다. 본 논문에서는 Jin et al.의 방법을 적용하여 홍채 템플릿을 보호하는 방법을 제안한다. 실험 결과는 이전의 폐기가능한 홍채인증 방법과 비교했을 때 더 높은 정확도를 보여주며 보안 및 프라이버시 공격에 강력함을 보여준다.

• 융합보안논문지
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• 제20권2호
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• pp.15-28
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• 2020

스마트시티는 기존 도시의 문제를 해결하고 새로운 부가가치를 창출할 것으로 큰 기대를 받고 있으나, 보안위험 증가에 우려 또한 증가하고 있다. 보안위험 인식에 따른 스마트시티에 대한 부정적 태도는 4차산업혁명 기술 활성화 및 스마트시티의 발전을 위해서도 개선해야 하는 문제이다. 이에 본 연구는 지각된 위험 이론과 사용자 저항이론을 바탕으로 스마트시티 내 위험요인과 스마트시티 저항 태도 간 관계를 통계적으로 분석하였다. 288명의 연구 표본을 대상으로 한 실증 분석결과에 따르면, 보안 위험, 사회적 위험, 신체적 위험요인은 스마트시티 저항에 직접적인 영향을 미치는 반면, 경제적, 성능, 사생활 침해에 대한 위험은 유의미한 영향이 없는 것으로 나타났다. 또한, 보안위험이 다른 위험에 선행요인으로 작용할 수 있다는 것을 검증하였으며, 스마트시티 환경에서는 사생활 침해 위험과 보안 위험에 대해 구분해서 논의할 필요성이 있음을 확인하였다. 본 연구는 일반인들 관점에서 스마트시티 인식에 부정적인 영향을 미치는 위험요인을 논의함으로써, 스마트시티 활성화를 위해 보안 및 안전 문제와 더불어 사회적 교류를 위한 정책적 방안 마련이 필요함을 보여준다.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.303-311
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• 2022

The tremendous growth of the Internet of things is unbelievable. Many IoT devices have emerged on the market over the last decade. This has made our everyday life easier inside our homes. The technology used at home has changed significantly over the past several decades, leading to what is known today as the smart home. However, this growth has also brought new challenges to our home security and privacy. With the smart home becoming more mainstream, cybersecurity issues have become a fundamental concern. The smart home is an environment where heterogeneous devices and appliances are interconnected through the Internet of Things (IoT) to provide smart services to residents. These services include home climate control, energy management, video on demand, music on-demand, remote healthcare, remote control, and other similar services in a ubiquitous manner. Smart home devices can be controlled via the Internet using smartphones. However, connecting smart home appliances to wireless networks and the Internet makes individuals vulnerable to malicious attacks. Remote access within the same environment or over the Internet requires an effective access control mechanism. This paper intends to shed light on how smart home devices are working as well as the type of security and privacy threats of the smart home. It also illustrated the types of authentication methods that can be used with smart home devices. In addition, a comparison of Smart home IoT-based security protocols was presented along with a security countermeasure that can be used in a smart home environment. Finally, a few open problems were mentioned as future research directions for researchers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.742-756
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• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• 지식경영연구
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• 제15권2호
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• pp.89-106
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• 2014

As an empirical study on the psychological side effects of using Social Networking Services (SNS), this study aims to identify the reality of negative emotion of using SNS and to predict its consequences. To this end, a measurement tool was developed through literature review, in-depth interview with users and expert review to induce negative emotional factors that can arise while using SNS. An exploratory factor analysis was performed for a total of 24 measurement items, which then were divided into the following 6 factors: 'concern over privacy,' 'burden from undesired connection,' 'relative deprivation,' 'a sense of alienation,' 'concern over reputation' and 'negative feeling about simple relationship.' Also, the relationship between the 6 negative emotional factors and psychological dissonance was analyzed. The results indicate that all the factors, except relative deprivation and a sense of alienation, affect psychological dissonance. It was also found that psychological dissonance, which implies a conflicting condition from using SNS, significantly affects the behavior that possibly reduces and limits the use of SNS. In other words, the users who have experienced psychological dissonance respond passively by avoiding the use of SNS to resolve the dissonance. The results of this study provide the base for explaining the psychological side effects of using SNS, which have been understood at a phenomenal level, such as 'Facebook depression' or 'SNS stress.' In addition, this study is of significance as it helps understand the psychological mechanism by identifying the relationship between negative emotion and use behavior with the theory of cognitive dissonance.

• 보건교육건강증진학회지
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• 제20권1호
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• pp.61-76
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• 2003

As people get broader access to health information through the internet, there is a greater need for measure to maximize the social advantages of the internet and to minimize negative side-effects. With this concern, this paper classifies internet health information services sites into : on-line supply of health information, on-line consultation, on-line diagnosis, and on-line sales. As well this paper analyzes domestic laws supporting and/or regulating these services. The efficient provision of internet health services requires comprehensive laws on individual privacy protection, prevention and handling of medical accidents, an electronic prescription form for internet diagnosis, electronic signing, payment for medical expenses, qualifications for internet medical practitioners. Additional laws are required to establish internet pharmacies and internet health goods stores. These new laws can be prepared either separately or through revision of existing laws governing medical practice, pharmacies, and public health promotion. However, as the legal control by the government on cyber processes and entities has a fairly minimal effect, consumers should be encouraged to improve their own capacity for wisely using internet health services and health-service providers should be encouraged to promote voluntary supervision and control of their own services and practices.

• 한국IT서비스학회지
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• 제19권5호
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• pp.15-31
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• 2020

This study explores the process in which employees adopt the information security policy. The results of this study, which surveyed 234 employees in three call centers and four hospitals, show that the employees adapt the information security policy through the social structuring process suggested by the AST model. In particular, this study identifies roles of two appropriation activities (FOA : Faithfulness of Appropriation & COA : Consensus on Appropriation) observed in the social structuring process. Regarding to the interactions between the two appropriation activities, FOA, which indicates a better understanding of the information security policy, is examined as a more critical factor than COA, which indicates the degree of agreement among employees about how to use it. FOA not only has a direct effect on compliance intention toward the information security policy, but also indirectly through COA, whereas COA has only a indirect effect through FOA. This result shows that, in order for a company to successfully implement a new information security policy, it is important for employees to understand its purpose and intention. The adaption of information security policy through two appropriation activities is observed in both hospitals and call centers, but due to the different working environments, there were differences in the preceding variables affecting the appropriation activities. The results of this study are expected to provide guidelines for companies who want to successfully adopt information security policy.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제9권12호
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• pp.291-306
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• 2020

오늘날 데이터 네트워크 AI (DNA) 기반 지능형 서비스 및 애플리케이션은 비즈니스의 삶의 질과 생산성을 향상시키는 새로운 차원의 서비스를 제공하는 것이 현실이 되었다. 인공지능(AI)은 IoT 데이터(IoT 장치에서 수집한 데이터)의 가치를 높이며, 사물 인터넷(IoT)은 AI의 학습 및 지능 기능을 촉진한다. 딥러닝을 사용하여 대량의 IoT 데이터에서 실시간으로 인사이트를 추출하려면 데이터가 생성되는 IoT 단말 장치에서의 처리능력이 필요하다. 그러나 딥러닝에는 IoT 최종 장치에서 사용할 수 없는 상당 수의 컴퓨팅 리소스가 필요하다. 이러한 문제는 처리를 위해 IoT 최종 장치에서 클라우드 데이터 센터로 대량의 데이터를 전송함으로써 해결되었다. 그러나 IoT 빅 데이터를 클라우드로 전송하면 엄청나게 높은 전송 지연과 주요 관심사인 개인 정보 보호 문제가 발생한다. 분산 컴퓨팅 노드가 IoT 최종 장치 가까이에 배치되는 엣지 컴퓨팅은 높은 계산 및 짧은 지연 시간 요구 사항을 충족하고 사용자의 개인 정보를 보호하는 실행 가능한 솔루션이다. 본 논문에서는 엣지 컴퓨팅 내에서 딥러닝을 활용하여 IoT 최종 장치에서 생성된 IoT 빅 데이터의 잠재력을 발휘하는 현재 상태에 대한 포괄적인 검토를 제공한다. 우리는 이것이 DNA 기반 지능형 서비스 및 애플리케이션 개발에 기여할 것이라고 본다. 엣지 컴퓨팅 플랫폼의 여러 노드에서 딥러닝 모델의 다양한 분산 교육 및 추론 아키텍처를 설명하고 엣지 컴퓨팅 환경과 네트워크 엣지에서 딥러닝이 유용할 수 있는 다양한 애플리케이션 도메인에서 딥러닝의 다양한 개인 정보 보호 접근 방식을 제공한다. 마지막으로 엣지 컴퓨팅 내에서 딥러닝을 활용하는 열린 문제와 과제에 대해 설명한다.

• 한국멀티미디어학회논문지
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• 제16권10호
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• pp.1156-1162
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• 2013

Face verification has been widely studied during the past two decades. One of the challenges is the rising concern about the security and privacy of the template database. In this paper, we propose a secure face verification system which generates a unique secure cryptographic key from a face template. The face images are processed to produce face templates or codes to be utilized for the encryption and decryption tasks. The result identity data is encrypted using Advanced Encryption Standard (AES). Distance metric naming hamming distance and Euclidean distance are used for template matching identification process, where template matching is a process used in pattern recognition. The proposed system is tested on the ORL, YALEs, and PKNU face databases, which contain 360, 135, and 54 training images respectively. We employ Principle Component Analysis (PCA) to determine the most discriminating features among face images. The experimental results showed that the proposed distance measure was one the promising best measures with respect to different characteristics of the biometric systems. Using the proposed method we needed to extract fewer images in order to achieve 100% cumulative recognition than using any other tested distance measure.

• 마케팅과학연구
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• 제7권
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• pp.321-337
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• 2001

본 연구의 옥적은 한국적 상황에서 전자상거래의 소비자 구매행위에 영향을 미치는 요인을 실증적으로 밝히기 위한 것이다. 이를 위하여 선행연구에서 구매행위에 영향을 미치는 것으로 밝혀진 온라인 기업의 특성, 거래 안정성의 우려, 프라이버시의 보장, 쇼핑성향, 인 지된 유통구조를 독립변수로 사용하였고, 소비자의 구매행위를 종속변수로 설정하여 다중 회귀분석을 실시하였다. 가설 검증 결과, 인지된 유통구조와 소비자의 오락적 성향과 경험 적 성향이 구매행위에 유의적인 정의 영향을 미치는 것으로 밝혀졌고, 거래 안정성의 우려 및 개인 정보의 누출은 구매행위에 유의적인 부의 영향을 미치는 것으로 밝혀졌다.