5 citations · 8 across the 3 of their papers we have counts for
3 papers
On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach
Mohit Kumar, Bernhard A. Moser, Lukas Fischer
Privacy-utility tradeoff remains as one of the fundamental issues of differentially private machine learning. This paper introduces a geometrically inspired kernel-based approach t…
Membership-Mappings for Practical Secure Distributed Deep Learning
Mohit Kumar, Weiping Zhang, Lukas Fischer +1
This study leverages the data representation capability of fuzzy based membership-mappings for practical secure distributed deep learning using fully homomorphic encryption. The im…
Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI
Mohit Kumar, Bernhard A. Moser, Lukas Fischer +1
In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic trustworthy AI f…