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Found 13 papers, 7 papers with code
Understanding Practical Membership Privacy of Deep Learning
We apply a state-of-the-art membership inference attack (MIA) to systematically test the practical privacy vulnerability of fine-tuning large image classification models. We focus on understanding the properties of data sets and samples that make them vulnerable to membership inference.
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation
The remaining subsampling-induced variance decreases with larger batch sizes, so large batches reduce the effective total gradient variance.
Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data
Consider a setting where multiple parties holding sensitive data aim to collaboratively learn population level statistics, but pooling the sensitive data sets is not possible.
DPVIm: Differentially Private Variational Inference Improved
Using this as prior knowledge we establish a link between the gradients of the variational parameters, and propose an efficient while simple fix for the problem to obtain a less noisy gradient estimator, which we call $\textit{aligned}$ gradients.
Noise-Aware Statistical Inference with Differentially Private Synthetic Data
For example, confidence intervals become too narrow, which we demonstrate with a simple experiment.
Locally Differentially Private Bayesian Inference
In recent years, local differential privacy (LDP) has emerged as a technique of choice for privacy-preserving data collection in several scenarios when the aggregator is not trustworthy.
Differentially Private Bayesian Inference for Generalized Linear Models
Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst's repertoire and often used on sensitive datasets.
Privacy-preserving Data Sharing on Vertically Partitioned Data
We modify a secure multiparty computation (MPC) framework to combine MPC with differential privacy (DP), in order to use differentially private MPC effectively to learn a probabilistic generative model under DP on such vertically partitioned data.
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT
We carry out an error analysis of the method in terms of moment bounds of the privacy loss distribution which leads to rigorous lower and upper bounds for the true $(\varepsilon,\delta)$-values.
Privacy-preserving data sharing via probabilistic modelling
Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data.
Computing Tight Differential Privacy Guarantees Using FFT
The privacy loss of DP algorithms is commonly reported using $(\varepsilon,\delta)$-DP.
Differentially Private Markov Chain Monte Carlo
Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects.
Differentially Private Variational Inference for Non-conjugate Models
It is built on top of doubly stochastic variational inference, a recent advance which provides a variational solution to a large class of models.
