한국정보처리학회:학술대회논문집 (Proceedings of the Korea Information Processing Society Conference)

한국정보처리학회 (Korea Information Processing Society)
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프라이버시 보존 머신러닝의 연구 동향

A Study on Privacy Preserving Machine Learning

  • 한우림 (서울대학교 전기.정보공학부, 서울대학교 반도체 공동연구소) ;
  • 이영한 (서울대학교 전기.정보공학부, 서울대학교 반도체 공동연구소) ;
  • 전소희 (서울대학교 전기.정보공학부, 서울대학교 반도체 공동연구소) ;
  • 조윤기 (서울대학교 전기.정보공학부, 서울대학교 반도체 공동연구소) ;
  • 백윤흥 (서울대학교 전기.정보공학부, 서울대학교 반도체 공동연구소)
  • Han, Woorim (Dept. of Electrical and Computer Engineering and Inter-University Semiconductor Research Center (ISRC), Seoul National University) ;
  • Lee, Younghan (Dept. of Electrical and Computer Engineering and Inter-University Semiconductor Research Center (ISRC), Seoul National University) ;
  • Jun, Sohee (Dept. of Electrical and Computer Engineering and Inter-University Semiconductor Research Center (ISRC), Seoul National University) ;
  • Cho, Yungi (Dept. of Electrical and Computer Engineering and Inter-University Semiconductor Research Center (ISRC), Seoul National University) ;
  • Paek, Yunheung (Dept. of Electrical and Computer Engineering and Inter-University Semiconductor Research Center (ISRC), Seoul National University)
  • 발행 : 2021.11.04
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초록

AI (Artificial Intelligence) is being utilized in various fields and services to give convenience to human life. Unfortunately, there are many security vulnerabilities in today's ML (Machine Learning) systems, causing various privacy concerns as some AI models need individuals' private data to train them. Such concerns lead to the interest in ML systems which can preserve the privacy of individuals' data. This paper introduces the latest research on various attacks that infringe data privacy and the corresponding defense techniques.

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과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1A2B5B03095204) and by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2018-0-00230, Development on Autonomous Trust Enhancement Technology of IoT Device and Study on Adaptive IoT Security Open Architecture based on Global Standardization [TrusThingz Project]). This work was also supported by the BK21 FOUR program of the Education and Research Program for Future ICT Pioneers, Seoul National University in 2021 and by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2020-0-00325, Traceability Assuarance Technology Development for Full Lifecycle Data Safety of Cloud Edge)