Privacy-preserving techniques for decentralized and secure machine learning in drug discovery
- Author(s)
- Aljoša Smajić, Melanie Grandits, Gerhard F Ecker
- Abstract
Data availability, data security, and privacy concerns often hamper optimal performance efficiency of machine learning (ML) techniques. Therefore, novel techniques for the utilization of private/sensitive data in the field of drug discovery have been proposed for ML model-building tasks. Some examples of the different techniques are secure multiparty computation, distributed deep learning, homomorphic encryption, blockchain-based peer-to-peer networking, differential privacy, and federated learning, as well as combinations of such techniques. In this paper, we present an overview of these techniques for decentralized ML to illustrate its benefits and drawbacks in the field of drug discovery.
- Organisation(s)
- Department of Pharmaceutical Sciences
- External organisation(s)
- Universität Wien
- Journal
- Drug Discovery Today
- Volume
- 28
- Pages
- 1-8
- No. of pages
- 8
- ISSN
- 1359-6446
- DOI
- https://doi.org/10.1016/j.drudis.2023.103820
- Publication date
- 11-2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 301207 Pharmaceutical chemistry
- ASJC Scopus subject areas
- Drug Discovery, Pharmacology
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/privacypreserving-techniques-for-decentralized-and-secure-machine-learning-in-drug-discovery(3e7235f5-c322-44a7-a598-e3d0ad4d1f85).html