Feb 14, 2023 · We investigate this further in the context of DP-SGD, a standard algorithm for private deep learning, and provide an upper bound on the success ...
Feb 8, 2024 · The goal of reconstruction is to extract a target record with limited prior knowledge about the record, i.e., reconstruct the record from scratch. The prior- ...
In this paper we re-visit the question of training data reconstruction against image classification models trained with DP-SGD (Song et al., 2013; Abadi et al., ...
May 30, 2024 · Differentially private training offers a protection which is usually interpreted as a guarantee against membership inference attacks.
In this paper, we derive the first semantic guarantees for DP mechanisms against training data reconstruction attacks under a formal threat model.
Bounding Training Data Reconstruction in Private (Deep) Learning. This repository contains code for reproducing results in the paper.
Feb 14, 2023 · We study the difficulties in learning that arise from robust and differentially private optimization. We first study convergence of gradient ...
In this paper, we derive semantic guarantees for DP mechanisms against training data reconstruction attacks under a formal threat model. We show that two ...
[PDF] Bounding Training Data Reconstruction in Private (Deep ...
icml.cc › media › icml-2022 › Slides
Bounding Training Data Reconstruction in Private (Deep) Learning. Page 2. Motivation. Differential privacy has been the de facto standard for data privacy. What ...
In this paper, we derive the first semantic guarantees for DP mechanisms against training data reconstruction attacks under a formal threat model. We show that ...