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

  • 2021.11a
  • /
  • Pages.61-63
  • /
  • 2021
  • /
  • 2005-0011(pISSN)
  • /
  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Protecting Privacy of Machine Learning Models

  • Lee, Younghan (Dept. of Electrical and Computer Engineering and Inter-University Semiconductor Research Center (ISRC), Seoul National University) ;
  • Han, Woorim (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) ;
  • Kim, Hyunjun (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)
  • Published : 2021.11.04
Download PDF
⟨ Previous Next ⟩

Abstract

Machine learning model gained the popularity in recent years as multi-national companies have incorporated machine learning in their services. Such service is called machine learning as a service (MLaSS). Such services are provided to users based on charge-per-query which triggers the motivations for adversaries to steal the trained victim model to reduce the cost of using the service. Therefore, it is important for companies that provide MLaSS to protect their intellectual property (IP) against adversaries. It has been arms race between the attack and defence in a context of the privacy of machine learning models. In this paper, we provide a comprehensive study of recent development in protecting privacy of machine learning models.

Keywords

Acknowledgement

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)