certified-unlearning-neural-networks-icml-2025

Certified Unlearning for Neural Networks This repository contains the implementation for the paper "Certified Unlearning for Neural Networks" ICML 2025. Methods PABI Privacy Amplification by Iteration : Gradient Clipping in the paper DP-SGD : With group privacy as described in the paper Contracti...

Exploring the Empirical Privacy Landscape of Synthetic Data

Methods for training language models on sensitive data differ in how they balance capability gains against the risk of leaking private information. In this paper, we present a controlled comparison of data-level and training-level interventions for navigating the capability-privacy tradeoff on...

Privacy, Robustness, and Fairness Trade-Offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface

Federated learning enables privacy-conscious collaboration for network intrusion detection without centralizing sensitive traffic data, yet its deployment in operational environments must simultaneously satisfy three competing requirements: formal differential privacy guaranties, tolerance to...