52 citations · 53 across the 3 of their papers we have counts for
3 papers
Private and Fair Machine Learning: Revisiting the Disparate Impact of Differentially Private SGD
Lea Demelius, Dominik Kowald, Simone Kopeinik +2
Differential privacy (DP) is a prominent method for protecting information about individuals during data analysis. Training neural networks with differentially private stochastic g…
Establishing and Evaluating Trustworthy AI: Overview and Research Challenges
Dominik Kowald, Sebastian Scher, Viktoria Pammer-Schindler +13
Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable out…
Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey
Lea Demelius, Roman Kern, Andreas Trügler
Differential Privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees. This…