Tootfinder

Minimax Data Sanitization with Distortion Constraint and Adversarial Inference
Amirarsalan Moatazedian, Yauhen Yakimenka, R\'emi A. Chou, J\"org Kliewer
https://arxiv.org/abs/2507.17942

Minimax Data Sanitization with Distortion Constraint and Adversarial Inference
We study a privacy-preserving data-sharing setting where a privatizer transforms private data into a sanitized version observed by an authorized reconstructor and two unauthorized adversaries, each with access to side information correlated with the private data. The reconstructor is evaluated under a distortion function, while each adversary is evaluated using a separate loss function. The privatizer ensures the reconstructor distortion remains below a fixed threshold while maximizing the mi…

Optimal differentially private kernel learning with random projection
Bonwoo Lee, Cheolwoo Park, Jeongyoun Ahn
https://arxiv.org/abs/2507.17544 https://

Optimal differentially private kernel learning with random projection
Differential privacy has become a cornerstone in the development of privacy-preserving learning algorithms. This work addresses optimizing differentially private kernel learning within the empirical risk minimization (ERM) framework. We propose a novel differentially private kernel ERM algorithm based on random projection in the reproducing kernel Hilbert space using Gaussian processes. Our method achieves minimax-optimal excess risk for both the squared loss and Lipschitz-smooth convex loss fu…