One-shot Empirical Privacy Estimation for Federated Learning

6 February 2023

Papers citing "One-shot Empirical Privacy Estimation for Federated Learning"

8 / 8 papers shown

Title
Towards One-shot Federated Learning: Advances, Challenges, and Future Directions Flora Amato Lingyu Qiu Mohammad Tanveer S. Cuomo F. Giampaolo F. Piccialli FedML 55 0 0 05 May 2025
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 85 0 0 02 Dec 2024
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD Thomas Steinke Milad Nasr Arun Ganesh Borja Balle Christopher A. Choquette-Choo Matthew Jagielski Jamie Hayes Abhradeep Thakurta Adam Smith Andreas Terzis 23 7 0 08 Oct 2024
Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective Héber H. Arcolezi Sébastien Gambs 25 1 0 04 Sep 2023
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 54 50 0 02 Oct 2022
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
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 178 154 0 26 Feb 2021
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