One-shot Empirical Privacy Estimation for Federated Learning

One-shot Empirical Privacy Estimation for Federated Learning

6 February 2023

Vinith M. Suriyakumar

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

9 / 9 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 58 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 34 7 0 08 Oct 2024
Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective Héber H. Arcolezi Sébastien Gambs 32 1 0 04 Sep 2023
Epsilon*: Privacy Metric for Machine Learning Models Diana M. Negoescu H. González Saad Eddin Al Orjany Jilei Yang Yuliia Lut ... Xinyi Zheng Zachariah Douglas Vidita Nolkha P. Ahammad G. Samorodnitsky 28 2 0 21 Jul 2023
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 56 49 0 02 Oct 2022
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri L. V. D. van der Maaten 115 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 182 154 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 290 1,814 0 14 Dec 2020