How Private are DP-SGD Implementations?

How Private are DP-SGD Implementations?

26 March 2024

Pasin Manurangsi

Papers citing "How Private are DP-SGD Implementations?"

15 / 15 papers shown

Title
Empirical Privacy Variance Yuzheng Hu Fan Wu Ruicheng Xian Yuhang Liu Lydia Zakynthinou Pritish Kamath Chiyuan Zhang David A. Forsyth 64 0 0 16 Mar 2025
Leveraging Randomness in Model and Data Partitioning for Privacy Amplification Andy Dong Wei-Ning Chen Ayfer Özgür FedML 54 1 0 04 Mar 2025
On the Differential Privacy and Interactivity of Privacy Sandbox Reports Badih Ghazi Charlie Harrison Arpana Hosabettu Pritish Kamath Alexander Knop ... Ethan Leeman Pasin Manurangsi Vikas Sahu Vikas Sahu Phillipp Schoppmann 94 1 0 22 Dec 2024
Balls-and-Bins Sampling for DP-SGD Lynn Chua Badih Ghazi Charlie Harrison Ethan Leeman Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 80 3 0 21 Dec 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 36 11 0 10 Jul 2024
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning Lynn Chua Badih Ghazi Yangsibo Huang Pritish Kamath Ravi Kumar Daogao Liu Pasin Manurangsi Amer Sinha Chiyuan Zhang 24 11 0 20 Jun 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition C. Lebeda Matthew Regehr Gautam Kamath Thomas Steinke 50 9 0 27 May 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang Ashwinee Panda Milad Nasr Saeed Mahloujifar Prateek Mittal 44 18 0 09 Jan 2024
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
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy Zhiqi Bu J. Mao Shiyun Xu 131 47 0 21 May 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 134 346 0 13 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 156 349 0 25 Sep 2021
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
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 147 178 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 144 420 0 29 Nov 2018