한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제25권4호
  • /
  • Pages.113-121
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  • 2020
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Privacy-Preserving Method to Collect Health Data from Smartband

  • Moon, Su-Mee (Dept. of Computer Science, Sangmyung University) ;
  • Kim, Jong-Wook (Dept. of Computer Science, Sangmyung University)
  • 투고 : 2020.02.24
  • 심사 : 2020.04.16
  • 발행 : 2020.04.29
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초록

센서 기술의 발전과 스마트 워치, 스마트 밴드와 같은 웨어러블 기기의 보편화로 개인의 건강데이터를 실시간으로 수집하는 일이 가능해졌다. 웨어러블 기기에서 파생된 걸음 수, 심박 수와 같은 건강 데이터들은 모바일 환경의 위치, 날씨 데이터 등의 외부 데이터와 결합하여, 개인의 라이프 스타일 및 건강 상태를 분석하는 방식으로 활용되고 있다. 이처럼 웨어러블 기기에서 파생된 건강 데이터는 편리하고 유용한 기능을 제공하지만 개인의 생활과 밀접한 연관이 있기 때문에 외부에 노출될 경우 심각한 프라이버시 침해 문제가 발생한다. 이에 본 연구는 지역차분프라이버시와 특징점 추출 알고리즘을 사용하여, 웨어러블 기기에서 추출한 건강 데이터를 데이터 소유자의 프라이버시 침해 없이 데이터 수집가에게 전송할 수 있는 기법을 소개한다. 지역차분프라이버시를 통해 데이터 소유자의 프라이버시를 보호하였으며 특징점 알고리즘으로 프라이버시 보호 수준과 데이터 유용성간의 상충 관계를 조절하였다. 실험 결과는 제안하는 기법이 단순 방법에 비해 최대 77% 정도의 오차율 개선이 있음을 보여준다. 수집된 데이터는 데이터 사용자의 요구에 따라 헬스 케어 및 맞춤형 서비스 산업에서 유의미하게 활용될 수 있다.

With the rapid development of information and communication technology (ICT), various sensors are being embedded in wearable devices. Consequently, these devices can continuously collect data including health data from individuals. The collected health data can be used not only for healthcare services but also for analyzing an individual's lifestyle by combining with other external data. This helps in making an individual's life more convenient and healthier. However, collecting health data may lead to privacy issues since the data is personal, and can reveal sensitive insights about the individual. Thus, in this paper, we present a method to collect an individual's health data from a smart band in a privacy-preserving manner. We leverage the local differential privacy to achieve our goal. Additionally, we propose a way to find feature points from health data. This allows for an effective trade-off between the degree of privacy and accuracy. We carry out experiments to demonstrate the effectiveness of our proposed approach and the results show that, with the proposed method, the error rate can be reduced upto 77%.

키워드

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