융합보안논문지 (Convergence Security Journal)

  • 제21권3호
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  • Pages.49-56
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  • 2021
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  • 1598-7329(pISSN)
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  • 2508-7460(eISSN)
한국융합보안학회 (Korea convergence Security Association)
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프라이버시 침해에 대응하는 분할 학습 모델 연구

A Study of Split Learning Model to Protect Privacy

  • 유지현 (성균관대학교 소프트웨어학과) ;
  • 원동호 (성균관대학교 소프트웨어학과) ;
  • 이영숙 (호원대학교 IT소프트웨어보안학과)
  • 투고 : 2021.09.01
  • 심사 : 2021.09.30
  • 발행 : 2021.09.30
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초록

현대의 인공지능은 사회를 구성하는 필수적인 기술로 여겨지고 있다. 특히, 인공지능에서 프라이버시 침해 문제는 현대 사회에서 심각한 문제로 자리 잡고 있다. 개인정보보호를 위해 2019년 MIT에서 제안된 분할 학습은 연합 학습의 기술 중 하나로 개인정보보호 효과를 지닌다. 본 연구에서는 데이터를 안전하게 관리하기 위해 알려진 차분 프라이버시를 이용하여 안전하고 정확한 분할 학습 모델을 연구한다. 또한, SVHN과 GTSRB 데이터 세트를 15가지의 차등적인 차분 프라이버시를 적용한 분할 학습 모델에 학습시키고 학습이 안정적으로 되는지를 확인한다. 최종적으로, 학습 데이터 추출 공격을 진행하여, 공격을 예방하는 차분 프라이버시 예산을 MSE를 통해 정량적으로 도출한다.

Recently, artificial intelligence is regarded as an essential technology in our society. In particular, the invasion of privacy in artificial intelligence has become a serious problem in modern society. Split learning, proposed at MIT in 2019 for privacy protection, is a type of federated learning technique that does not share any raw data. In this study, we studied a safe and accurate segmentation learning model using known differential privacy to safely manage data. In addition, we trained SVHN and GTSRB on a split learning model to which 15 different types of differential privacy are applied, and checked whether the learning is stable. By conducting a learning data extraction attack, a differential privacy budget that prevents attacks is quantitatively derived through MSE.

키워드

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