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Found 11 papers, 5 papers with code
Privacy Profiles for Private Selection
Private selection mechanisms (e. g., Report Noisy Max, Sparse Vector) are fundamental primitives of differentially private (DP) data analysis with wide applications to private query release, voting, and hyperparameter tuning.
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
In the arena of privacy-preserving machine learning, differentially private stochastic gradient descent (DP-SGD) has outstripped the objective perturbation mechanism in popularity and interest.
Practical Differentially Private Hyperparameter Tuning with Subsampling
Tuning the hyperparameters of differentially private (DP) machine learning (ML) algorithms often requires use of sensitive data and this may leak private information via hyperparameter values.
Individual Privacy Accounting with Gaussian Differential Privacy
In order to account for the individual privacy losses in a principled manner, we need a privacy accountant for adaptive compositions of randomised mechanisms, where the loss incurred at a given data access is allowed to be smaller than the worst-case loss.
Tight Accounting in the Shuffle Model of Differential Privacy
Shuffle model of differential privacy is a novel distributed privacy model based on a combination of local privacy mechanisms and a secure shuffler.
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT
The recently proposed Fast Fourier Transform (FFT)-based accountant for evaluating $(\varepsilon,\delta)$-differential privacy guarantees using the privacy loss distribution formalism has been shown to give tighter bounds than commonly used methods such as R\'enyi accountants when applied to homogeneous compositions, i. e., to compositions of identical mechanisms.
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.
Differentially private cross-silo federated learning
In this paper we combine additively homomorphic secure summation protocols with differential privacy in the so-called cross-silo federated learning setting.
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.
Computing Tight Differential Privacy Guarantees Using FFT
The privacy loss of DP algorithms is commonly reported using $(\varepsilon,\delta)$-DP.
Learning Rate Adaptation for Federated and Differentially Private Learning
We also show that it works robustly in the case of federated learning unlike commonly used optimisation methods.
