20 citations · 26 across the 15 of their papers we have counts for
10 papers · 1 filter
Memorisation bias in medical AI
Moritz A. Knolle, Martin J. Menten, Laurin Lux +5
Medical AI models hold immense potential to improve patient outcomes, but they are also known to unintentionally memorise individual records from their training datasets. While suc…
Sensitivity, Specificity, and Consistency: A Tripartite Evaluation of Privacy Filters for Synthetic Data Generation
Adil Koeken, Alexander Ziller, Moritz Knolle +1
The generation of privacy-preserving synthetic datasets is a promising avenue for overcoming data scarcity in medical AI research. Post-hoc privacy filtering techniques, designed t…
Visual Privacy Auditing with Diffusion Models
Kristian Schwethelm, Johannes Kaiser, Moritz Knolle +3
Data reconstruction attacks on machine learning models pose a substantial threat to privacy, potentially leaking sensitive information. Although defending against such attacks usin…
SoK: Memorisation in machine learning
Dmitrii Usynin, Moritz Knolle, Georgios Kaissis
Quantifying the impact of individual data samples on machine learning models is an open research problem. This is particularly relevant when complex and high-dimensional relationsh…
(Predictable) Performance Bias in Unsupervised Anomaly Detection
Felix Meissen, Svenja Breuer, Moritz Knolle +5
Background: With the ever-increasing amount of medical imaging data, the demand for algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models promi…
Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD
Moritz Knolle, Robert Dorfman, Alexander Ziller +2
Differentially private SGD (DP-SGD) holds the promise of enabling the safe and responsible application of machine learning to sensitive datasets. However, DP-SGD only provides a bi…
An automatic differentiation system for the age of differential privacy
Dmitrii Usynin, Alexander Ziller, Moritz Knolle +4
We introduce Tritium, an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML). Optimal noise calibration in this set…
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation
Alexander Ziller, Dmitrii Usynin, Moritz Knolle +6
In recent years, formal methods of privacy protection such as differential privacy (DP), capable of deployment to data-driven tasks such as machine learning (ML), have emerged. Rec…
NeuralDP Differentially private neural networks by design
Moritz Knolle, Dmitrii Usynin, Alexander Ziller +3
The application of differential privacy to the training of deep neural networks holds the promise of allowing large-scale (decentralized) use of sensitive data while providing rigo…
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty
Moritz Knolle, Alexander Ziller, Dmitrii Usynin +4
We show that differentially private stochastic gradient descent (DP-SGD) can yield poorly calibrated, overconfident deep learning models. This represents a serious issue for safety…