Scalable Private Learning with PATE

24 February 2018

Papers citing "Scalable Private Learning with PATE"

50 / 153 papers shown

Title
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 25 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 75 102 0 30 Jun 2022
Self-Supervised Pretraining for Differentially Private Learning Arash Asadian Evan Weidner Lei Jiang PICV 29 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 56 22 0 16 May 2022
Synthetic Data -- what, why and how? James Jordon Lukasz Szpruch F. Houssiau M. Bottarelli Giovanni Cherubin Carsten Maple Samuel N. Cohen Adrian Weller 48 109 0 06 May 2022
Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems Shail Dave Alberto Marchisio Muhammad Abdullah Hanif Amira Guesmi Aviral Shrivastava Ihsen Alouani Muhammad Shafique 34 13 0 18 Apr 2022
A Differentially Private Framework for Deep Learning with Convexified Loss Functions Zhigang Lu Hassan Jameel Asghar M. Kâafar Darren Webb Peter Dickinson 80 15 0 03 Apr 2022
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE Yuqing Zhu Yu-Xiang Wang 37 18 0 30 Mar 2022
FedVLN: Privacy-preserving Federated Vision-and-Language Navigation Kaiwen Zhou Xinze Wang FedML 28 8 0 28 Mar 2022
FLDP: Flexible strategy for local differential privacy Danting Zhao Suyun Zhao Ruixuan Liu Cuiping Li Wenjuan Liang Hong Chen 21 3 0 28 Mar 2022
One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy Dayong Ye Sheng Shen Tianqing Zhu B. Liu Wanlei Zhou MIACV 16 62 0 13 Mar 2022
Differentially Private Label Protection in Split Learning Xin Yang Jiankai Sun Yuanshun Yao Junyuan Xie Chong-Jun Wang FedML 44 36 0 04 Mar 2022
MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members Ismat Jarin Birhanu Eshete 32 9 0 02 Mar 2022
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey Ferdinando Fioretto Cuong Tran Pascal Van Hentenryck Keyu Zhu FaML 32 60 0 16 Feb 2022
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning A. Mondal Harpreet Virk Debayan Gupta 40 15 0 06 Feb 2022
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation Wenxiao Wang Alexander Levine S. Feizi AAML 20 60 0 05 Feb 2022
A Secure and Efficient Federated Learning Framework for NLP Jieren Deng Chenghong Wang Xianrui Meng Yijue Wang Ji Li Sheng Lin Shuo Han Fei Miao Sanguthevar Rajasekaran Caiwen Ding FedML 77 22 0 28 Jan 2022
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges Nuria Rodríguez-Barroso Daniel Jiménez López M. V. Luzón Francisco Herrera Eugenio Martínez-Cámara FedML 37 212 0 20 Jan 2022
SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation Yash More Prashanthi Ramachandran Priyam Panda A. Mondal Harpreet Virk Debayan Gupta FedML 27 11 0 19 Jan 2022
Reconstructing Training Data with Informed Adversaries Borja Balle Giovanni Cherubin Jamie Hayes MIACV AAML 50 159 0 13 Jan 2022
Gradient Leakage Attack Resilient Deep Learning Wenqi Wei Ling Liu SILM PILM AAML 27 47 0 25 Dec 2021
Membership Inference Attacks From First Principles Nicholas Carlini Steve Chien Milad Nasr Shuang Song Andreas Terzis Florian Tramèr MIACV MIALM 29 646 0 07 Dec 2021
Public Data-Assisted Mirror Descent for Private Model Training Ehsan Amid Arun Ganesh Rajiv Mathews Swaroop Indra Ramaswamy Shuang Song Thomas Steinke Vinith Suriyakumar Om Thakkar Abhradeep Thakurta 21 49 0 01 Dec 2021
Privately Publishable Per-instance Privacy Rachel Redberg Yu-Xiang Wang 37 17 0 03 Nov 2021
Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection Chunjong Park Anas Awadalla Tadayoshi Kohno Shwetak N. Patel OOD 30 29 0 26 Oct 2021
SEDML: Securely and Efficiently Harnessing Distributed Knowledge in Machine Learning Yansong Gao Qun Li Yifeng Zheng Guohong Wang Jiannan Wei Mang Su 32 3 0 26 Oct 2021
DP-XGBoost: Private Machine Learning at Scale Cheng Cheng Wei Dai 22 8 0 25 Oct 2021
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 134 350 0 13 Oct 2021
NanoBatch Privacy: Enabling fast Differentially Private learning on the IPU Edward H. Lee M. M. Krell Alexander Tsyplikhin Victoria Rege E. Colak Kristen W. Yeom FedML 21 0 0 24 Sep 2021
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data Georgi Ganev Bristena Oprisanu Emiliano De Cristofaro 37 57 0 23 Sep 2021
SoK: Machine Learning Governance Varun Chandrasekaran Hengrui Jia Anvith Thudi Adelin Travers Mohammad Yaghini Nicolas Papernot 40 16 0 20 Sep 2021
Releasing Graph Neural Networks with Differential Privacy Guarantees Iyiola E. Olatunji Thorben Funke Megha Khosla 37 45 0 18 Sep 2021
A Fairness Analysis on Private Aggregation of Teacher Ensembles Cuong Tran M. H. Dinh Kyle Beiter Ferdinando Fioretto 21 12 0 17 Sep 2021
Membership Inference Attacks Against Recommender Systems Minxing Zhang Z. Ren Zihan Wang Pengjie Ren Zhumin Chen Pengfei Hu Yang Zhang MIACV AAML 26 83 0 16 Sep 2021
Selective Differential Privacy for Language Modeling Weiyan Shi Aiqi Cui Evan Li R. Jia Zhou Yu 20 68 0 30 Aug 2021
Order Optimal Bounds for One-Shot Federated Learning over non-Convex Loss Functions Arsalan Sharifnassab Saber Salehkaleybar S. J. Golestani FedML 11 0 0 19 Aug 2021
Privacy-Preserving Machine Learning: Methods, Challenges and Directions Runhua Xu Nathalie Baracaldo J. Joshi 32 100 0 10 Aug 2021
Survey: Leakage and Privacy at Inference Time Marija Jegorova Chaitanya Kaul Charlie Mayor Alison Q. OÑeil Alexander Weir Roderick Murray-Smith Sotirios A. Tsaftaris PILM MIACV 23 71 0 04 Jul 2021
Smoothed Differential Privacy Ao Liu Yu-Xiang Wang Lirong Xia 33 0 0 04 Jul 2021
Gradient-Leakage Resilient Federated Learning Wenqi Wei Ling Liu Yanzhao Wu Gong Su Arun Iyengar FedML 19 81 0 02 Jul 2021
Faithful Edge Federated Learning: Scalability and Privacy Meng Zhang Ermin Wei R. Berry FedML 26 44 0 30 Jun 2021
Large Scale Private Learning via Low-rank Reparametrization Da Yu Huishuai Zhang Wei Chen Jian Yin Tie-Yan Liu 29 100 0 17 Jun 2021
Adaptive Machine Unlearning Varun Gupta Christopher Jung Seth Neel Aaron Roth Saeed Sharifi-Malvajerdi Chris Waites MU 25 174 0 08 Jun 2021
Antipodes of Label Differential Privacy: PATE and ALIBI Mani Malek Ilya Mironov Karthik Prasad I. Shilov Florian Tramèr 16 62 0 07 Jun 2021
Membership Inference Attack Susceptibility of Clinical Language Models Abhyuday N. Jagannatha Bhanu Pratap Singh Rawat Hong-ye Yu MIACV 29 62 0 16 Apr 2021
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation Wei Ping Fan Wu Yunhui Long Luka Rimanic Ce Zhang Bo-wen Li FedML 45 63 0 20 Mar 2021
Efficient Encrypted Inference on Ensembles of Decision Trees Kanthi Kiran Sarpatwar Karthik Nandakumar Nalini Ratha J. Rayfield Karthikeyan Shanmugam Sharath Pankanti Roman Vaculin FedML 22 5 0 05 Mar 2021