Scalable Private Learning with PATE

24 February 2018

Papers citing "Scalable Private Learning with PATE"

50 / 138 papers shown

Title
DPolicy: Managing Privacy Risks Across Multiple Releases with Differential Privacy Nicolas Küchler Alexander Viand Hidde Lycklama Anwar Hithnawi 26 0 0 10 May 2025
A Comprehensive Survey of Synthetic Tabular Data Generation Ruxue Shi Yili Wang Mengnan Du Xu Shen Xin Wang 49 2 0 23 Apr 2025
DP-GPL: Differentially Private Graph Prompt Learning Jing Xu Franziska Boenisch Iyiola Emmanuel Olatunji Adam Dziedzic AAML 53 0 0 13 Mar 2025
A Tale of Two Imperatives: Privacy and Explainability Supriya Manna Niladri Sett 109 0 0 30 Dec 2024
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning Fengyu Gao Ruida Zhou T. Wang Cong Shen Jing Yang 37 2 0 15 Oct 2024
PFGuard: A Generative Framework with Privacy and Fairness Safeguards Soyeon Kim Yuji Roh Geon Heo Steven Euijong Whang 39 0 0 03 Oct 2024
Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation Shiming Ge Bochao Liu Pengju Wang Yong Li Dan Zeng FedML 44 9 0 04 Sep 2024
PATE-TripleGAN: Privacy-Preserving Image Synthesis with Gaussian Differential Privacy Zepeng Jiang Weiwei Ni Yifan Zhang PICV 21 1 0 19 Apr 2024
VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication Xun Yuan Yang Yang P. Gope A. Pasikhani Biplab Sikdar 42 2 0 15 Apr 2024
Privacy Preserving Prompt Engineering: A Survey Kennedy Edemacu Xintao Wu 47 18 0 09 Apr 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations Enayat Ullah Michael Menart Raef Bassily Cristóbal Guzmán Raman Arora 30 1 0 06 Mar 2024
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance Wenqi Wei Ling Liu 31 16 0 02 Feb 2024
Machine unlearning through fine-grained model parameters perturbation Zhiwei Zuo Zhuo Tang KenLi Li Anwitaman Datta AAML MU 26 0 0 09 Jan 2024
Hot PATE: Private Aggregation of Distributions for Diverse Task Edith Cohen Benjamin Cohen-Wang Xin Lyu Jelani Nelson Tamas Sarlos Uri Stemmer 52 3 0 04 Dec 2023
Forgetting Private Textual Sequences in Language Models via Leave-One-Out Ensemble Zhe Liu Ozlem Kalinli MU KELM 28 2 0 28 Sep 2023
Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David Alex Bie C. Canonne Gautam Kamath Vikrant Singhal 42 11 0 11 Aug 2023
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency Jiawei Shao Zijian Li Wenqiang Sun Tailin Zhou Yuchang Sun Lumin Liu Zehong Lin Yuyi Mao Jun Zhang FedML 43 23 0 20 Jul 2023
Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning Tamas Madl Weijie Xu Olivia Choudhury Matthew Howard 31 5 0 04 Jul 2023
Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization Zhigang Kan Linbo Qiao Hao Yu Liwen Peng Yifu Gao Dongsheng Li 28 20 0 14 Jun 2023
PILLAR: How to make semi-private learning more effective Francesco Pinto Yaxian Hu Fanny Yang Amartya Sanyal 49 11 0 06 Jun 2023
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zinan Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 38 19 0 23 May 2023
On the Fairness Impacts of Private Ensembles Models Cuong Tran Ferdinando Fioretto 41 4 0 19 May 2023
Privacy-Preserving Ensemble Infused Enhanced Deep Neural Network Framework for Edge Cloud Convergence Veronika Stephanie I. Khalil Mohammad Saidur Rahman Mohammed Atiquzzaman FedML 13 10 0 16 May 2023
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs Brian M. Belgodere Pierre L. Dognin Adam Ivankay Igor Melnyk Youssef Mroueh ... Mattia Rigotti Jerret Ross Yair Schiff Radhika Vedpathak Richard A. Young 32 12 0 21 Apr 2023
30 Years of Synthetic Data Joerg Drechsler Anna Haensch 30 15 0 04 Apr 2023
From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification Dominique Mercier Andreas Dengel Sheraz Ahmed AI4TS 20 0 0 28 Mar 2023
Considerations on the Theory of Training Models with Differential Privacy Marten van Dijk Phuong Ha Nguyen FedML 8 2 0 08 Mar 2023
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning Van Tuan Tran Huy Hieu Pham Kok-Seng Wong FedML 36 7 0 22 Feb 2023
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility Mohammad Yaghini Patty Liu Franziska Boenisch Nicolas Papernot FedML FaML 29 8 0 17 Feb 2023
An Empirical Analysis of Fairness Notions under Differential Privacy Anderson Santana de Oliveira Caelin Kaplan Khawla Mallat Tanmay Chakraborty FedML 21 7 0 06 Feb 2023
HEAR4Health: A blueprint for making computer audition a staple of modern healthcare Andreas Triantafyllopoulos Alexander Kathan Alice Baird Lukas Christ Alexander Gebhard ... Shahin Amiriparian K. D. Bartl-Pokorny A. Batliner Florian B. Pokorny Björn W. Schuller 47 7 0 25 Jan 2023
Achieving Transparency in Distributed Machine Learning with Explainable Data Collaboration A. Bogdanova A. Imakura T. Sakurai Tomoya Fujii Teppei Sakamoto Hiroyuki Abe FedML 19 2 0 06 Dec 2022
Private Multi-Winner Voting for Machine Learning Adam Dziedzic Christopher A. Choquette-Choo Natalie Dullerud Vinith Suriyakumar Ali Shahin Shamsabadi Muhammad Ahmad Kaleem S. Jha Nicolas Papernot Xiao Wang 42 1 0 23 Nov 2022
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 32 14 0 21 Nov 2022
DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder Bjarne Pfitzner B. Arnrich FedML 33 19 0 21 Nov 2022
A Survey on Differential Privacy with Machine Learning and Future Outlook Samah Baraheem Z. Yao SyDa 11 1 0 19 Nov 2022
Private Set Generation with Discriminative Information Dingfan Chen Raouf Kerkouche Mario Fritz DD 30 34 0 07 Nov 2022
Revisiting Hyperparameter Tuning with Differential Privacy Youlong Ding Xueyang Wu 21 0 0 03 Nov 2022
Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels Qiuchen Zhang Jing Ma Jian Lou Li Xiong Xiaoqian Jiang NoLa 21 0 0 03 Nov 2022
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling Jianxin Wei Ergute Bao X. Xiao Yifan Yang 46 20 0 18 Oct 2022
Mitigating Unintended Memorization in Language Models via Alternating Teaching Zhe Liu Xuedong Zhang Fuchun Peng 32 3 0 13 Oct 2022
An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition Chao-Han Huck Yang Jun Qi Sabato Marco Siniscalchi Chin-Hui Lee 26 4 0 12 Oct 2022
An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition Chao-Han Huck Yang I-Fan Chen A. Stolcke Sabato Marco Siniscalchi Chin-Hui Lee 32 2 0 11 Oct 2022
In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning Jiaqi Wang R. Schuster Ilia Shumailov David Lie Nicolas Papernot FedML 33 3 0 22 Sep 2022
Private Estimation with Public Data Alex Bie Gautam Kamath Vikrant Singhal 36 28 0 16 Aug 2022
Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning Phung Lai Han Hu Nhathai Phan Ruoming Jin My T. Thai An M. Chen 20 2 0 26 Jul 2022
Measuring Forgetting of Memorized Training Examples Matthew Jagielski Om Thakkar Florian Tramèr Daphne Ippolito Katherine Lee ... Eric Wallace Shuang Song Abhradeep Thakurta Nicolas Papernot Chiyuan Zhang TDI 66 102 0 30 Jun 2022
Self-Supervised Pretraining for Differentially Private Learning Arash Asadian Evan Weidner Lei Jiang PICV 27 3 0 14 Jun 2022
Differential Privacy: What is all the noise about? Roxana Dánger Mercaderes 38 3 0 19 May 2022
On the Difficulty of Defending Self-Supervised Learning against Model Extraction Adam Dziedzic Nikita Dhawan Muhammad Ahmad Kaleem Jonas Guan Nicolas Papernot MIACV 54 22 0 16 May 2022