34 citations · 86 across the 12 of their papers we have counts for
12 papers
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…
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…
Privacy Leakage via Output Label Space and Differentially Private Continual Learning
Marlon Tobaben, Talal Alrawajfeh, Marcus Klasson +3
Differential privacy (DP) is a formal privacy framework that enables training machine learning (ML) models while protecting individuals' data. As pointed out by prior work, ML mode…
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
Luca Corbucci, Mikko A Heikkila, David Solans Noguero +2
Training and deploying Machine Learning models that simultaneously adhere to principles of fairness and privacy while ensuring good utility poses a significant challenge. The inter…
On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps
Mikko A. Heikkilä
Federated learning is a distributed learning setting where the main aim is to train machine learning models without having to share raw data but only what is required for learning.…
Speech Robust Bench: A Robustness Benchmark For Speech Recognition
Muhammad A. Shah, David Solans Noguero, Mikko A. Heikkila +2
As Automatic Speech Recognition (ASR) models become ever more pervasive, it is important to ensure that they make reliable predictions under corruptions present in the physical and…
Differentially private partitioned variational inference
Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop +2
Learning a privacy-preserving model from sensitive data which are distributed across multiple devices is an increasingly important problem. The problem is often formulated in the f…
Tight Accounting in the Shuffle Model of Differential Privacy
Antti Koskela, Mikko A. Heikkilä, Antti Honkela
Shuffle model of differential privacy is a novel distributed privacy model based on a combination of local privacy mechanisms and a secure shuffler. It has been shown that the addi…
Differentially private cross-silo federated learning
Mikko A. Heikkilä, Antti Koskela, Kana Shimizu +2
Strict privacy is of paramount importance in distributed machine learning. Federated learning, with the main idea of communicating only what is needed for learning, has been recent…
Representation Transfer for Differentially Private Drug Sensitivity Prediction
Teppo Niinimäki, Mikko Heikkilä, Antti Honkela +1
Motivation: Human genomic datasets often contain sensitive information that limits use and sharing of the data. In particular, simple anonymisation strategies fail to provide suffi…
Differentially Private Markov Chain Monte Carlo
Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen +1
Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects. I…
Differentially Private Bayesian Learning on Distributed Data
Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski +3
Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become esta…