Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning

12 November 2022

Christopher A. Choquette-Choo

Papers citing "Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning"

36 / 36 papers shown

Title
Binned Group Algebra Factorization for Differentially Private Continual Counting Monika Henzinger Nikita P. Kalinin Jalaj Upadhyay 26 0 0 06 Apr 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
Data value estimation on private gradients Zijian Zhou Xinyi Xu Daniela Rus Bryan Kian Hsiang Low 72 0 0 22 Dec 2024
Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture Mohamad Haj Fares Ahmed Mohamed Saad Emam Saad OOD MedIm 68 1 0 01 Dec 2024
DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing Alexander Bienstock Ujjwal Kumar Antigoni Polychroniadou FedML 27 0 0 21 Oct 2024
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 36 5 0 08 Oct 2024
Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning Xiaowen Fu Bingxin Wang Xinzhou Guo Guoqing Liu Yang Xiang 17 0 0 20 Sep 2024
A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs H. B. McMahan Zheng Xu Yanxiang Zhang FedML 32 5 0 16 Aug 2024
Fine-Tuning Large Language Models with User-Level Differential Privacy Zachary Charles Arun Ganesh Ryan McKenna H. B. McMahan Nicole Mitchell Krishna Pillutla Keith Rush 21 11 0 10 Jul 2024
Continual Counting with Gradual Privacy Expiration Joel Daniel Andersson Monika Henzinger Rasmus Pagh Teresa Anna Steiner Jalaj Upadhyay 43 1 0 06 Jun 2024
Click Without Compromise: Online Advertising Measurement via Per User Differential Privacy Yingtai Xiao Jian Du Shikun Zhang Qiang Yan Danfeng Zhang Daniel Kifer Daniel Kifer 32 2 0 04 Jun 2024
Banded Square Root Matrix Factorization for Differentially Private Model Training Nikita Kalinin Christoph H. Lampert 26 5 0 22 May 2024
Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy Wei-Ning Chen Berivan Isik Peter Kairouz Albert No Sewoong Oh Zheng Xu 39 3 0 02 May 2024
Teach LLMs to Phish: Stealing Private Information from Language Models Ashwinee Panda Christopher A. Choquette-Choo Zhengming Zhang Yaoqing Yang Prateek Mittal PILM 21 20 0 01 Mar 2024
Auditing Private Prediction Karan Chadha Matthew Jagielski Nicolas Papernot Christopher A. Choquette-Choo Milad Nasr 23 4 0 14 Feb 2024
Robust and Actively Secure Serverless Collaborative Learning Olive Franzese Adam Dziedzic Christopher A. Choquette-Choo Mark R. Thomas Muhammad Ahmad Kaleem Stephan Rabanser Cong Fang Somesh Jha Nicolas Papernot Xiao Wang OOD 17 2 0 25 Oct 2023
Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Thomas Steinke Abhradeep Thakurta 20 9 0 24 Oct 2023
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning Christopher A. Choquette-Choo Krishnamurthy Dvijotham Krishna Pillutla Arun Ganesh Thomas Steinke Abhradeep Thakurta 17 13 0 10 Oct 2023
Privacy Side Channels in Machine Learning Systems Edoardo Debenedetti Giorgio Severi Nicholas Carlini Christopher A. Choquette-Choo Matthew Jagielski Milad Nasr Eric Wallace Florian Tramèr MIALM 29 38 0 11 Sep 2023
Private Federated Learning with Autotuned Compression Enayat Ullah Christopher A. Choquette-Choo Peter Kairouz Sewoong Oh FedML 11 6 0 20 Jul 2023
A Unifying Framework for Differentially Private Sums under Continual Observation Monika Henzinger Jalaj Upadhyay Sarvagya Upadhyay FedML 24 14 0 18 Jul 2023
A Smooth Binary Mechanism for Efficient Private Continual Observation Joel Daniel Andersson Rasmus Pagh 13 11 0 16 Jun 2023
(Amplified) Banded Matrix Factorization: A unified approach to private training Christopher A. Choquette-Choo Arun Ganesh Ryan McKenna H. B. McMahan Keith Rush Abhradeep Thakurta Zheng Xu FedML 13 35 0 13 Jun 2023
Federated Learning of Gboard Language Models with Differential Privacy Zheng Xu Yanxiang Zhang Galen Andrew Christopher A. Choquette-Choo Peter Kairouz H. B. McMahan Jesse Rosenstock Yuanbo Zhang FedML 24 76 0 29 May 2023
An Empirical Evaluation of Federated Contextual Bandit Algorithms Alekh Agarwal H. B. McMahan Zheng Xu FedML 11 2 0 17 Mar 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 94 167 0 01 Mar 2023
On the Convergence of Federated Averaging with Cyclic Client Participation Yae Jee Cho Pranay Sharma Gauri Joshi Zheng Xu Satyen Kale Tong Zhang FedML 22 27 0 06 Feb 2023
One-shot Empirical Privacy Estimation for Federated Learning Galen Andrew Peter Kairouz Sewoong Oh Alina Oprea H. B. McMahan Vinith M. Suriyakumar FedML 13 32 0 06 Feb 2023
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy Anastasia Koloskova Ryan McKenna Zachary B. Charles Keith Rush Brendan McMahan 27 8 0 02 Feb 2023
General Gaussian Noise Mechanisms and Their Optimality for Unbiased Mean Estimation Aleksandar Nikolov Haohua Tang 26 4 0 31 Jan 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 13 18 0 22 Jan 2023
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation Ljubomir Rokvic Panayiotis Danassis Sai Praneeth Karimireddy Boi Faltings TDI 18 1 0 23 May 2022
Constant matters: Fine-grained Complexity of Differentially Private Continual Observation Hendrik Fichtenberger Monika Henzinger Jalaj Upadhyay 24 20 0 23 Feb 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
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 134 420 0 29 Nov 2018