212 citations · 505 across the 43 of their papers we have counts for
6 papers · 1 filter
Locally Differentially Private Bayesian Inference
Tejas Kulkarni, Joonas Jälkö, Samuel Kaski +1
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 trustw…
Differentially Private Hamiltonian Monte Carlo
Ossi Räisä, Antti Koskela, Antti Honkela
Markov chain Monte Carlo (MCMC) algorithms have long been the main workhorses of Bayesian inference. Among them, Hamiltonian Monte Carlo (HMC) has recently become very popular due…
Tight Accounting in the Shuffle Model of Differential Privacy
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. It has been shown that the addi…
Gaussian Processes with Differential Privacy
Antti Honkela, Laila Melkas
Gaussian processes (GPs) are non-parametric Bayesian models that are widely used for diverse prediction tasks. Previous work in adding strong privacy protection to GPs via differen…
D3p -- A Python Package for Differentially-Private Probabilistic Programming
Lukas Prediger, Niki Loppi, Samuel Kaski +1
We present d3p, a software package designed to help fielding runtime efficient widely-applicable Bayesian inference under differential privacy guarantees. d3p achieves general appl…
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT
Antti Koskela, Antti Honkela
The recently proposed Fast Fourier Transform (FFT)-based accountant for evaluating -differential privacy guarantees using the privacy loss distribution formalism h…