19 citations · 36 across the 7 of their papers we have counts for
7 papers
Privacy Profiles for Private Selection
Antti Koskela, Rachel Redberg, Yu-Xiang Wang
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…
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
Rachel Redberg, Antti Koskela, Yu-Xiang Wang
In the arena of privacy-preserving machine learning, differentially private stochastic gradient descent (DP-SGD) has outstripped the objective perturbation mechanism in popularity…
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
Yingyu Lin, Yi-An Ma, Yu-Xiang Wang +2
Posterior sampling, i.e., exponential mechanism to sample from the posterior distribution, provides -pure differential privacy (DP) guarantees and does not suffer from…
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy
Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang
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 th…
Differentially Private Linear Sketches: Efficient Implementations and Applications
Fuheng Zhao, Dan Qiao, Rachel Redberg +3
Linear sketches have been widely adopted to process fast data streams, and they can be used to accurately answer frequency estimation, approximate top K items, and summarize data d…
Privately Publishable Per-instance Privacy
Rachel Redberg, Yu-Xiang Wang
We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP). Standard differential privacy…
Tree++: Truncated Tree Based Graph Kernels
Wei Ye, Zhen Wang, Rachel Redberg +1
Graph-structured data arise ubiquitously in many application domains. A fundamental problem is to quantify their similarities. Graph kernels are often used for this purpose, which…