Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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User Authentication Key Establishment Scheme based on Color Model for Healthcare Environment

헬스케어 환경을 위한 칼라 모델 기반의 사용자 인증 키 설립 기법

  • Jeong, Yoon-Su (Dept. of information Communication Convergence Engineering, Mokwon University)
  • 정윤수 (목원대학교 정보통신융합공학부)
  • Received : 2017.01.31
  • Accepted : 2017.03.20
  • Published : 2017.03.28
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Abstract

Hospital medical services are making great efforts to provide prompt medical services to patients or improve the quality of medical services by convergence patient's healthcare information. However, recent research suggests problems about safety and efficiency when trying to transmit patient's healthcare information to hospital server via radio and wireless. In this paper, we propose a color model - based patient authentication key establishment protocol method to securely transmit patient healthcare information. The proposed method extracts randomly three color information used in the color model and vectorizes the extracted arbitrary information to obtain the key information required for user authentication as the sum of orthogonal vectors to improve the efficiency. In addition, the proposed method can securely generate key information used for user authentication without using an additional encryption algorithm. In performance evaluation result, proposed method shows that the server processing time of the sensed information is 8.1% higher than the existing method and 7.7% lower than the existing method.

병원의료 서비스는 사용자의 헬스케어 정보를 융합하여 신속한 의료서비스를 사용자에게 제공하거나 의료서비스의 질 개선을 위해서 많은 노력을 하고 있다. 그러나, 최근 연구에서는 사용자의 헬스케어 정보를 유 무선을 통해 병원 서버에 전달하려고 할 때, 사용자의 헬스케어 정보가 노출되는 문제점이 있다. 본 논문에서는 사용자의 헬스케어 정보를 안전하게 전달하기 위한 칼라 모델 기반의 사용자 인증 키 설립 프로토콜 기법을 제안한다. 제안 기법은 칼라 모델에서 사용되는 칼라 정보를 랜덤하게 3개 추출하여 추출된 임의의 정보를 벡터화하여 사용자 인증에 필요한 키 정보를 직교 벡터의 합으로 구함으로써 효율성을 높이는 것을 목적으로 한다. 또한, 제안 기법은 추가적인 암호 알고리즘을 사용하지 않으면서 사용자 인증에 필요한 키 정보를 안전하게 생성할 수 있다. 성능 평가 결과, 제안 기법은 사용자의 헬스케어 정보의 수가 증가할수록 생성된 정보를 처리하는 서버의 시간이 기존 기법보다 평균 8.1% 낮게 나타났으며, 오버헤드는 기존 기법보다 7.7% 낮은 결과를 얻었다.

Keywords

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