How to DP-fy ML: A Practical Guide to Machine Learning with Differential
Privacy

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy

1 March 2023

Natalia Ponomareva

Hussein Hazimeh

Carson E. Denison

Sergei Vassilvitskii

Abhradeep Thakurta

Papers citing "How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy"

17 / 17 papers shown

Title
DeSIA: Attribute Inference Attacks Against Limited Fixed Aggregate Statistics Yifeng Mao Bozhidar Stevanoski Yves-Alexandre de Montjoye 35 0 0 25 Apr 2025
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators Tejumade Afonja Hui-Po Wang Raouf Kerkouche Mario Fritz SyDa 82 2 0 03 Dec 2024
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri L. V. D. van der Maaten 73 47 0 28 Jan 2022
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 110 258 0 13 Oct 2021
Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective J. Zhang Haochuan Li S. Sra Ali Jadbabaie 46 8 0 12 Oct 2021
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 96 98 0 07 Oct 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 121 268 0 25 Sep 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 144 358 0 14 Jul 2021
Deduplicating Training Data Makes Language Models Better Katherine Lee Daphne Ippolito A. Nystrom Chiyuan Zhang Douglas Eck Chris Callison-Burch Nicholas Carlini SyDa 219 447 0 14 Jul 2021
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 148 154 0 26 Feb 2021
Permute-and-Flip: A new mechanism for differentially private selection Ryan McKenna Daniel Sheldon 82 43 0 23 Oct 2020
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 110 155 0 28 Jul 2020
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 105 384 0 29 Nov 2018
Prochlo: Strong Privacy for Analytics in the Crowd Andrea Bittau Ulfar Erlingsson Petros Maniatis Ilya Mironov A. Raghunathan David Lie Mitch Rudominer Ushasree Kode J. Tinnés B. Seefeld 54 276 0 02 Oct 2017
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu M. Schuster Z. Chen Quoc V. Le Mohammad Norouzi ... Alex Rudnick Oriol Vinyals G. Corrado Macduff Hughes J. Dean AIMat 708 6,435 0 26 Sep 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 262 2,696 0 15 Sep 2016
Efficient Per-Example Gradient Computations Ian Goodfellow 124 63 0 07 Oct 2015