1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.SI2025
On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models
Antti Koskela, Mohamed Seif, Andrea J. Goldsmith
We investigate privacy-preserving spectral clustering for community detection within stochastic block models (SBMs). Specifically, we focus on edge differential privacy (DP) and pr…
cs.CR2024
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…
cs.LG2023★ 1 cited
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…