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Found 5 papers, 2 papers with code
Differentially Private Latent Diffusion Models
In this paper, we further improve the current state of DMs with DP by adopting the Latent Diffusion Models (LDMs).
Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation
Data distillation aims to generate a small data set that closely mimics the performance of a given learning algorithm on the original data set.
Differentially private stochastic expectation propagation (DP-SEP)
We provide a theoretical analysis of the privacy-accuracy trade-off in the posterior estimates under our method, called differentially private stochastic expectation propagation (DP-SEP).
Hermite Polynomial Features for Private Data Generation
Hence, a relatively low order of Hermite polynomial features can more accurately approximate the mean embedding of the data distribution compared to a significantly higher number of random features.
ABCDP: Approximate Bayesian Computation with Differential Privacy
SVT incurs the privacy cost only when a condition (whether a quantity of interest is above/below a threshold) is met.
