4 papers
Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
Linzh Zhao, Aki Rehn, Mikko A. Heikkilä +2
Differential privacy (DP) has become an essential framework for privacy-preserving machine learning. Existing DP learning methods, however, often have disparate impacts on model pr…
Beyond Square Roots: Explicit Memory-Efficient Factorization for Multi-Epoch Private Learning
Nikita P. Kalinin, Aki Rehn, Joel Daniel Andersson +2
Correlated-noise mechanisms are among the most promising approaches for improving the utility of differentially private model training, but rigorous guarantees require explicit, an…
On Optimal Hyperparameters for Differentially Private Deep Transfer Learning
Aki Rehn, Linzh Zhao, Mikko A. Heikkilä +1
Differentially private (DP) transfer learning, i.e., fine-tuning a pretrained model on private data, is the current state-of-the-art approach for training large models under privac…
An Interactive Framework for Finding the Optimal Trade-off in Differential Privacy
Yaohong Yang, Aki Rehn, Sammie Katt +2
Differential privacy (DP) is the standard for privacy-preserving analysis, and introduces a fundamental trade-off between privacy guarantees and model performance. Selecting the op…