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.
|Title of host publication||4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2022|
|Event||4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Jeju lsland, Korea, Republic of|
Duration: 2022 Feb 21 → 2022 Feb 24
|Name||4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings|
|Conference||4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022|
|Country/Territory||Korea, Republic of|
|Period||22/2/21 → 22/2/24|
Bibliographical noteFunding Information:
This research was supported by the MOTIE (Ministry of Trade, Industry, and Energy) in Korea, under the Fostering Global Talents for Innovative Growth Program (P0008749) supervised by the Korea Institute for Advancement of Technology (KIAT) and National Research Foundation of Korea (NRF-2019R1A2C1084778).
© 2022 IEEE.
- 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