3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.CR2025
Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning
Linda Scheu-Hachtel, Jasmin Zalonis
Functional encryption (FE) has recently attracted interest in privacy-preserving machine learning (PPML) for its unique ability to compute specific functions on encrypted data. A r…
cs.CR2022★ 3 cited
Report: State of the Art Solutions for Privacy Preserving Machine Learning in the Medical Context
Jasmin Zalonis, Frederik Armknecht, Björn Grohmann +1
Machine Learning on Big Data gets more and more attention in various fields. Even so privacy-preserving techniques become more important, even necessary due to legal regulations su…