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Found 6 papers, 1 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.
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
Posterior sampling, i. e., exponential mechanism to sample from the posterior distribution, provides $\varepsilon$-pure differential privacy (DP) guarantees and does not suffer from potentially unbounded privacy breach introduced by $(\varepsilon,\delta)$-approximate DP.
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy
The ''Propose-Test-Release'' (PTR) framework is a classic recipe for designing differentially private (DP) algorithms that are data-adaptive, i. e. those that add less noise when the input dataset is nice.
Privately Publishable Per-instance Privacy
We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP).
Tree++: Truncated Tree Based Graph Kernels
At the heart of Tree++ is a graph kernel called the path-pattern graph kernel.
