Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data
The results show that our approach effectively prevents privacy breaches while only moderately decreasing the inference quality.
Authors
- Valentin Henkys (ORCID: https://orcid.org/0009-0002-9379-7129)
- Bertil Schmidt (ORCID: https://orcid.org/0000-0003-2597-8331)
- Konstantin Strauch (ORCID: https://orcid.org/0000-0002-1814-5860)
- Daan Apeldoorn (ORCID: https://orcid.org/0000-0001-5170-5893)
- Torsten Panholzer (ORCID: https://orcid.org/0000-0002-2808-6120)
- Xenia Heilmann (ORCID: https://orcid.org/0000-0003-3758-9253)
- Timm Lilienthal
Institutions
- Johannes Gutenberg University Mainz (DE)
- University Medical Center of the Johannes Gutenberg University Mainz (DE)
Publication Details
- Journal
- Studies in health technology and informatics
- Published
- 2024-08-30
- DOI
- https://doi.org/10.3233/shti240866
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 0.00