Bounding Training Data Reconstruction in DP-SGD

14 February 2023

Papers citing "Bounding Training Data Reconstruction in DP-SGD"

28 / 28 papers shown

Title
Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security Yiwei Zhang R. Behnia A. Yavuz Reza Ebrahimi E. Bertino FedML 21 0 0 09 May 2025
Empirical Calibration and Metric Differential Privacy in Language Models Pedro Faustini Natasha Fernandes Annabelle McIver Mark Dras 60 0 0 18 Mar 2025
Empirical Privacy Variance Yuzheng Hu Fan Wu Ruicheng Xian Yuhang Liu Lydia Zakynthinou Pritish Kamath Chiyuan Zhang David A. Forsyth 62 0 0 16 Mar 2025
$(\varepsilon, δ)$ Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees Juan Felipe Gomez B. Kulynych G. Kaissis Jamie Hayes Borja Balle Antti Honkela 51 0 0 13 Mar 2025
Analyzing Inference Privacy Risks Through Gradients in Machine Learning Zhuohang Li Andrew Lowy Jing Liu T. Koike-Akino K. Parsons Bradley Malin Ye Wang FedML 23 1 0 29 Aug 2024
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective Zifan Wang Binghui Zhang Meng Pang Yuan Hong Binghui Wang FedML 29 0 0 22 Aug 2024
Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement Jeremiah Birrell Reza Ebrahimi R. Behnia Jason L. Pacheco 23 0 0 19 Aug 2024
Private Collaborative Edge Inference via Over-the-Air Computation Selim F. Yilmaz Burak Hasircioglu Li Qiao Deniz Gunduz FedML 48 0 0 30 Jul 2024
Attack-Aware Noise Calibration for Differential Privacy B. Kulynych Juan Felipe Gomez G. Kaissis Flavio du Pin Calmon Carmela Troncoso 44 6 0 02 Jul 2024
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy Georgios Kaissis Stefan Kolek Borja Balle Jamie Hayes Daniel Rueckert 40 4 0 13 Jun 2024
Data Reconstruction: When You See It and When You Don't Edith Cohen Haim Kaplan Yishay Mansour Shay Moran Kobbi Nissim Uri Stemmer Eliad Tsfadia AAML 37 2 0 24 May 2024
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries Rachel Cummings Shlomi Hod Jayshree Sarathy Marika Swanberg 28 2 0 02 May 2024
Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures S. V. Dibbo Adam Breuer Juston S. Moore Michael Teti AAML 28 4 0 21 Mar 2024
Visual Privacy Auditing with Diffusion Models Kristian Schwethelm Johannes Kaiser Moritz Knolle Daniel Rueckert Daniel Rueckert Alexander Ziller DiffM AAML 31 0 0 12 Mar 2024
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach Qi Tan Qi Li Yi Zhao Zhuotao Liu Xiaobing Guo Ke Xu FedML 29 2 0 02 Mar 2024
Bounding Reconstruction Attack Success of Adversaries Without Data Priors Alexander Ziller Anneliese Riess Kristian Schwethelm Tamara T. Mueller Daniel Rueckert Georgios Kaissis MIACV AAML 24 1 0 20 Feb 2024
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks? Andrew Lowy Zhuohang Li Jing Liu T. Koike-Akino K. Parsons Ye Wang 11 9 0 14 Feb 2024
Data Reconstruction Attacks and Defenses: A Systematic Evaluation Sheng Liu Zihan Wang Yuxiao Chen Qi Lei AAML MIACV 54 4 0 13 Feb 2024
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging Alexander Ziller Tamara T. Mueller Simon Stieger Leonhard F. Feiner Johannes Brandt R. Braren Daniel Rueckert Georgios Kaissis 53 1 0 05 Dec 2023
SoK: Memorisation in machine learning Dmitrii Usynin Moritz Knolle Georgios Kaissis 9 1 0 06 Nov 2023
Understanding Deep Gradient Leakage via Inversion Influence Functions Haobo Zhang Junyuan Hong Yuyang Deng M. Mahdavi Jiayu Zhou FedML 42 6 0 22 Sep 2023
Unlocking Accuracy and Fairness in Differentially Private Image Classification Leonard Berrada Soham De J. Shen Jamie Hayes Robert Stanforth David Stutz Pushmeet Kohli Samuel L. Smith Borja Balle 19 13 0 21 Aug 2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy Georgios Kaissis Jamie Hayes Alexander Ziller Daniel Rueckert AAML 19 11 0 08 Jul 2023
Unleashing the Power of Randomization in Auditing Differentially Private ML Krishna Pillutla Galen Andrew Peter Kairouz H. B. McMahan Alina Oprea Sewoong Oh 17 20 0 29 May 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging Soroosh Tayebi Arasteh Alexander Ziller Christiane Kuhl Marcus R. Makowski S. Nebelung R. Braren Daniel Rueckert Daniel Truhn Georgios Kaissis MedIm 21 17 0 03 Feb 2023
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri L. V. D. van der Maaten 103 53 0 28 Jan 2022
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 267 1,808 0 14 Dec 2020
Individual Privacy Accounting via a Renyi Filter Vitaly Feldman Tijana Zrnic 46 86 0 25 Aug 2020