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01/27/2023
Practical Differentially Private Hyperparameter Tuning with Subsampling
Tuning all the hyperparameters of differentially private (DP) machine le...
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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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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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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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08/29/2020
Computing low-rank approximations of the Fréchet derivative of a matrix function using Krylov subspace methods
The Fréchet derivative L_f(A,E) of the matrix function f(A) plays an imp...
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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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04/12/2020
Sampling of Stochastic Differential Equations using the Karhunen-Loève Expansion and Matrix Functions
We consider linearizations of stochastic differential equations with add...
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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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09/11/2018
