Scalable Private Learning with PATE

24 February 2018

Papers citing "Scalable Private Learning with PATE"

50 / 138 papers shown

Title
Synthetic Data -- what, why and how? James Jordon Lukasz Szpruch F. Houssiau M. Bottarelli Giovanni Cherubin Carsten Maple Samuel N. Cohen Adrian Weller 43 109 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 57 15 0 03 Apr 2022
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE Yuqing Zhu Yu-Xiang Wang 35 18 0 30 Mar 2022
FedVLN: Privacy-preserving Federated Vision-and-Language Navigation Kaiwen Zhou Qing Guo FedML 26 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 61 0 13 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 29 60 0 16 Feb 2022
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning A. Mondal Harpreet Virk Debayan Gupta 37 15 0 06 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
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 43 158 0 13 Jan 2022
Gradient Leakage Attack Resilient Deep Learning Wenqi Wei Ling Liu SILM PILM AAML 27 46 0 25 Dec 2021
Membership Inference Attacks From First Principles Nicholas Carlini Steve Chien Milad Nasr Shuang Song Andreas Terzis Florian Tramèr MIACV MIALM 29 639 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 13 49 0 01 Dec 2021
Privately Publishable Per-instance Privacy Rachel Redberg Yu-Xiang Wang 32 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 134 347 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 38 16 0 20 Sep 2021
Releasing Graph Neural Networks with Differential Privacy Guarantees Iyiola E. Olatunji Thorben Funke Megha Khosla 32 44 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 6 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 21 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
Adaptive Machine Unlearning Varun Gupta Christopher Jung Seth Neel Aaron Roth Saeed Sharifi-Malvajerdi Chris Waites MU 16 174 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 60 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 N. Ratha J. Rayfield Karthikeyan Shanmugam Sharath Pankanti Roman Vaculin FedML 22 5 0 05 Mar 2021
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting Ismat Jarin Birhanu Eshete 26 18 0 19 Feb 2021
Deep Learning with Label Differential Privacy Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Chiyuan Zhang 42 144 0 11 Feb 2021
CaPC Learning: Confidential and Private Collaborative Learning Christopher A. Choquette-Choo Natalie Dullerud Adam Dziedzic Yunxiang Zhang S. Jha Nicolas Papernot Xiao Wang FedML 70 57 0 09 Feb 2021
Quantifying and Mitigating Privacy Risks of Contrastive Learning Xinlei He Yang Zhang 21 51 0 08 Feb 2021
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning Milad Nasr Shuang Song Abhradeep Thakurta Nicolas Papernot Nicholas Carlini MIACV FedML 67 216 0 11 Jan 2021
Kamino: Constraint-Aware Differentially Private Data Synthesis Chang Ge Shubhankar Mohapatra Xi He Ihab F. Ilyas SyDa 23 44 0 31 Dec 2020
Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators on Multi-Tenant FPGAs Andrew Boutros Mathew Hall Nicolas Papernot Vaughn Betz 16 38 0 14 Dec 2020
Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods James Jordon A. Wilson M. Schaar AI4CE 87 16 0 08 Dec 2020
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 183 355 0 07 Dec 2020
Free Gap Estimates from the Exponential Mechanism, Sparse Vector, Noisy Max and Related Algorithms Zeyu Ding Yuxin Wang Yingtai Xiao Guanhong Wang Danfeng Zhang Daniel Kifer 31 6 0 02 Dec 2020
On the Privacy Risks of Algorithmic Fairness Hong Chang Reza Shokri FaML 33 109 0 07 Nov 2020