Privacy-preserving collaborative machine learning in biomedical applications

Wonsuk Kim, Junhee Seok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Machine learning (ML) algorithms are now widely used to tackle computational problems in diverse domains. In biomedicine, the rapidly growing amounts of experimental data increasingly necessitate the use of ML to discern complex data patterns. However, biomedical data is often considered sensitive, and the privacy of individuals behind the data is increasingly put at risk as a result. Traditional methods such as anonymization and pseudonymization are not always applicable and have limited effectiveness with respect to risk mitigation. Privacy researchers are actively developing alternative approaches to privacy protection, including strategies based on cryptography, such as homomorphic encryption and secure multiparty computation. This paper discusses recent advances in biomedical applications of these privacy techniques. We first review the key privacy techniques, then provide an overview of their applications in biomedical machine learning. Finally, we highlight the remaining challenges of current approaches and suggest directions for future work.

Original languageEnglish
Title of host publication4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages179-183
Number of pages5
ISBN (Electronic)9781665458184
DOIs
Publication statusPublished - 2022
Event4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Jeju lsland, Korea, Republic of
Duration: 2022 Feb 212022 Feb 24

Publication series

Name4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings

Conference

Conference4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022
Country/TerritoryKorea, Republic of
CityJeju lsland
Period22/2/2122/2/24

Keywords

  • Collaborative Learning
  • Federated Learning
  • Privacy-Preserving Machine Learning
  • Secure Multi-party Computation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Electrical and Electronic Engineering

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