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

1 March 2023

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

50 / 106 papers shown

Title
Visual Privacy Auditing with Diffusion Models Kristian Schwethelm Johannes Kaiser Moritz Knolle Daniel Rueckert Daniel Rueckert Alexander Ziller DiffM AAML 40 0 0 12 Mar 2024
Pre-training Differentially Private Models with Limited Public Data Zhiqi Bu Xinwei Zhang Mingyi Hong Sheng Zha George Karypis 79 3 0 28 Feb 2024
Privacy-Preserving Instructions for Aligning Large Language Models Da Yu Peter Kairouz Sewoong Oh Zheng Xu 36 18 0 21 Feb 2024
You Still See Me: How Data Protection Supports the Architecture of ML Surveillance Rui-Jie Yew Lucy Qin Suresh Venkatasubramanian 41 3 0 09 Feb 2024
Privacy Profiles for Private Selection Antti Koskela Rachel Redberg Yu-Xiang Wang 36 1 0 09 Feb 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang Ashwinee Panda Milad Nasr Saeed Mahloujifar Prateek Mittal 50 18 0 09 Jan 2024
Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning Keyi Ju Xiaoqi Qin Hui Zhong Xinyue Zhang Miao Pan Baoling Liu 11 3 0 18 Dec 2023
An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification Prakhar Ganesh 47 5 0 24 Nov 2023
DP-NMT: Scalable Differentially-Private Machine Translation Timour Igamberdiev Doan Nam Long Vu Felix Künnecke Zhuo Yu Jannik Holmer Ivan Habernal 40 7 0 24 Nov 2023
Can we infer the presence of Differential Privacy in Deep Learning models' weights? Towards more secure Deep Learning Daniel Jiménez-López Daniel Nuria Rodríguez Barroso Nuria M. V. Luzón M. Victoria Francisco Herrera Francisco AAML 24 0 0 20 Nov 2023
Reducing Privacy Risks in Online Self-Disclosures with Language Models Yao Dou Isadora Krsek Tarek Naous Anubha Kabra Sauvik Das Alan Ritter Wei Xu 38 22 0 16 Nov 2023
Sparsity-Preserving Differentially Private Training of Large Embedding Models Badih Ghazi Yangsibo Huang Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 34 2 0 14 Nov 2023
DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning Wenxuan Bao Francesco Pittaluga Vijay Kumar Vincent Bindschaedler 23 9 0 02 Nov 2023
Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Thomas Steinke Abhradeep Thakurta 30 10 0 24 Oct 2023
Unintended Memorization in Large ASR Models, and How to Mitigate It Lun Wang Om Thakkar Rajiv Mathews 41 5 0 18 Oct 2023
User Inference Attacks on Large Language Models Nikhil Kandpal Krishna Pillutla Alina Oprea Peter Kairouz Christopher A. Choquette-Choo Zheng Xu SILM AAML 44 15 0 13 Oct 2023
A Survey of Data Security: Practices from Cybersecurity and Challenges of Machine Learning Padmaksha Roy Jaganmohan Chandrasekaran Erin Lanus Laura J. Freeman Jeremy Werner 30 3 0 06 Oct 2023
Can Language Models be Instructed to Protect Personal Information? Yang Chen Ethan Mendes Sauvik Das Wei Xu Alan Ritter PILM 27 35 0 03 Oct 2023
A Unified View of Differentially Private Deep Generative Modeling Dingfan Chen Raouf Kerkouche Mario Fritz SyDa 33 4 0 27 Sep 2023
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning Parham Gohari Matthew T. Hale Ufuk Topcu OffRL 32 1 0 13 Sep 2023
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks Daniel Scheliga Patrick Mäder M. Seeland FedML AAML 28 5 0 08 Sep 2023
Differentially Private Linear Regression with Linked Data Shurong Lin Elliot Paquette E. D. Kolaczyk 29 1 0 01 Aug 2023
Confidential Computing across Edge-to-Cloud for Machine Learning: A Survey Study S. Zobaed M. Salehi FedML 25 3 0 31 Jul 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 37 2 0 21 Jul 2023
The importance of feature preprocessing for differentially private linear optimization Ziteng Sun A. Suresh A. Menon 32 3 0 19 Jul 2023
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning Zachary B. Charles Nicole Mitchell Krishna Pillutla Michael Reneer Zachary Garrett FedML AI4CE 40 28 0 18 Jul 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 36 36 0 13 Jun 2023
Differentially Private One Permutation Hashing and Bin-wise Consistent Weighted Sampling Xiaoyun Li Ping Li 43 6 0 13 Jun 2023
Harnessing large-language models to generate private synthetic text Alexey Kurakin Natalia Ponomareva Umar Syed Liam MacDermed Andreas Terzis SILM SyDa 36 36 0 02 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 47 77 0 29 May 2023
Unleashing the Power of Randomization in Auditing Differentially Private ML Krishna Pillutla Galen Andrew Peter Kairouz H. B. McMahan Alina Oprea Sewoong Oh 43 20 0 29 May 2023
DP-SGD Without Clipping: The Lipschitz Neural Network Way Louis Bethune Thomas Massena Thibaut Boissin Yannick Prudent Corentin Friedrich Franck Mamalet A. Bellet M. Serrurier David Vigouroux 36 9 0 25 May 2023
Faster Differentially Private Convex Optimization via Second-Order Methods Arun Ganesh Mahdi Haghifam Thomas Steinke Abhradeep Thakurta 19 10 0 22 May 2023
Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models Aldo G. Carranza Rezsa Farahani Natalia Ponomareva Alexey Kurakin Matthew Jagielski Milad Nasr SyDa 28 7 0 10 May 2023
Balancing Privacy and Performance for Private Federated Learning Algorithms Xiangjiang Hou Sarit Khirirat Mohammad Yaqub Samuel Horváth FedML 30 0 0 11 Apr 2023
An Empirical Evaluation of Federated Contextual Bandit Algorithms Alekh Agarwal H. B. McMahan Zheng Xu FedML 34 2 0 17 Mar 2023
Private GANs, Revisited Alex Bie Gautam Kamath Guojun Zhang 38 14 0 06 Feb 2023
Private Ad Modeling with DP-SGD Carson E. Denison Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Krishnagiri Narra Amer Sinha A. Varadarajan Chiyuan Zhang 34 14 0 21 Nov 2022
Learning to Generate Image Embeddings with User-level Differential Privacy Zheng Xu Maxwell D. Collins Yuxiao Wang Liviu Panait Sewoong Oh S. Augenstein Ting Liu Florian Schroff H. B. McMahan FedML 37 29 0 20 Nov 2022
Provable Membership Inference Privacy Zachary Izzo Jinsung Yoon Sercan Ö. Arik James Zou 44 5 0 12 Nov 2022
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM Chulin Xie Pin-Yu Chen Qinbin Li Arash Nourian Ce Zhang Bo Li FedML 47 16 0 20 Jul 2022
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri Laurens van der Maaten 115 53 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 134 351 0 13 Oct 2021
Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective Jiaming Zhang Haochuan Li S. Sra Ali Jadbabaie 66 9 0 12 Oct 2021
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 135 120 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 168 350 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 187 412 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 242 599 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 184 194 0 26 Feb 2021
Permute-and-Flip: A new mechanism for differentially private selection Ryan McKenna Daniel Sheldon 112 47 0 23 Oct 2020