research
∙
08/09/2023
Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data
Consider a setting where multiple parties holding sensitive data aim to ...
share
research
∙
05/26/2023
On Consistent Bayesian Inference from Synthetic Data
Generating synthetic data, with or without differential privacy, has att...
research
∙
10/28/2022
DPVIm: Differentially Private Variational Inference Improved
Differentially private (DP) release of multidimensional statistics typic...
research
∙
05/28/2022
Noise-Aware Statistical Inference with Differentially Private Synthetic Data
While generation of synthetic data under differential privacy (DP) has r...
research
∙
10/27/2021
Locally Differentially Private Bayesian Inference
In recent years, local differential privacy (LDP) has emerged as a techn...
research
∙
11/01/2020
Differentially Private Bayesian Inference for Generalized Linear Models
The framework of differential privacy (DP) upper bounds the information ...
research
∙
10/19/2020
Privacy-preserving Data Sharing on Vertically Partitioned Data
In this work, we present a method for differentially private data sharin...
research
∙
06/12/2020
Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT
We propose a numerical accountant for evaluating the tight (ε,δ)-privacy...
research
∙
12/10/2019
Privacy-preserving data sharing via probabilistic modelling
Differential privacy allows quantifying privacy loss from computations o...
research
∙
06/07/2019
Computing Exact Guarantees for Differential Privacy
Quantification of the privacy loss associated with a randomised algorith...
research
∙
01/29/2019
Differentially Private Markov Chain Monte Carlo
Recent developments in differentially private (DP) machine learning and ...
research
∙
10/27/2016
