Differentially private partitioned variational inference

1 code implementation • 23 Sep 2022 • Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E. Turner, Antti Honkela

In this paper, we present differentially private partitioned variational inference, the first general framework for learning a variational approximation to a Bayesian posterior distribution in the federated learning setting while minimising the number of communication rounds and providing differential privacy guarantees for data subjects.

Federated Learning Privacy Preserving +1

Tight Accounting in the Shuffle Model of Differential Privacy

no code implementations • 1 Jun 2021 • 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.

Differentially private cross-silo federated learning

1 code implementation • 10 Jul 2020 • Mikko A. Heikkilä, Antti Koskela, Kana Shimizu, Samuel Kaski, Antti Honkela

In this paper we combine additively homomorphic secure summation protocols with differential privacy in the so-called cross-silo federated learning setting.

Federated Learning

Differentially Private Markov Chain Monte Carlo

1 code implementation • NeurIPS 2019 • Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela

Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects.