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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 ...
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05/26/2023
On Consistent Bayesian Inference from Synthetic Data
Generating synthetic data, with or without differential privacy, has att...
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02/02/2023
On the Efficacy of Differentially Private Few-shot Image Classification
There has been significant recent progress in training differentially pr...
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10/28/2022
DPVIm: Differentially Private Variational Inference Improved
Differentially private (DP) release of multidimensional statistics typic...
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09/30/2022
Individual Privacy Accounting with Gaussian Differential Privacy
Individual privacy accounting enables bounding differential privacy (DP)...
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09/23/2022
Differentially private partitioned variational inference
Learning a privacy-preserving model from distributed sensitive data is a...
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05/28/2022
Noise-Aware Statistical Inference with Differentially Private Synthetic Data
While generation of synthetic data under differential privacy (DP) has r...
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10/27/2021
Locally Differentially Private Bayesian Inference
In recent years, local differential privacy (LDP) has emerged as a techn...
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06/17/2021
Differentially Private Hamiltonian Monte Carlo
Markov chain Monte Carlo (MCMC) algorithms have long been the main workh...
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06/01/2021
Tight Accounting in the Shuffle Model of Differential Privacy
Shuffle model of differential privacy is a novel distributed privacy mod...
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06/01/2021
Gaussian Processes with Differential Privacy
Gaussian processes (GPs) are non-parametric Bayesian models that are wid...
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03/22/2021
d3p – A Python Package for Differentially-Private Probabilistic Programming
We present d3p, a software package designed to help fielding runtime eff...
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02/24/2021
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT
The recently proposed Fast Fourier Transform (FFT)-based accountant for ...
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11/01/2020
Differentially Private Bayesian Inference for Generalized Linear Models
The framework of differential privacy (DP) upper bounds the information ...
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10/19/2020
Privacy-preserving Data Sharing on Vertically Partitioned Data
In this work, we present a method for differentially private data sharin...
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07/10/2020
Differentially private cross-silo federated learning
Strict privacy is of paramount importance in distributed machine learnin...
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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...
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12/10/2019
Privacy-preserving data sharing via probabilistic modelling
Differential privacy allows quantifying privacy loss from computations o...
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11/24/2019
Differentially Private Federated Variational Inference
In many real-world applications of machine learning, data are distribute...
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06/07/2019
Computing Exact Guarantees for Differential Privacy
Quantification of the privacy loss associated with a randomised algorith...
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01/29/2019
Differentially Private Markov Chain Monte Carlo
Recent developments in differentially private (DP) machine learning and ...
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01/29/2019
Representation Transfer for Differentially Private Drug Sensitivity Prediction
Motivation: Human genomic datasets often contain sensitive information t...
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09/11/2018
Learning rate adaptation for differentially private stochastic gradient descent
Differentially private learning has recently emerged as the leading appr...
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03/03/2017
Differentially Private Bayesian Learning on Distributed Data
Many applications of machine learning, for example in health care, would...
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10/27/2016
Differentially Private Variational Inference for Non-conjugate Models
Many machine learning applications are based on data collected from peop...
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06/07/2016
Efficient differentially private learning improves drug sensitivity prediction
Users of a personalised recommendation system face a dilemma: recommenda...
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03/08/2016
On the inconsistency of ℓ_1-penalised sparse precision matrix estimation
Various ℓ_1-penalised estimation methods such as graphical lasso and CLI...
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10/09/2012
Gaussian process modelling of multiple short time series
We present techniques for effective Gaussian process (GP) modelling of m...
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07/04/2012
