Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

18 October 2016

Papers citing "Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data"

50 / 215 papers shown

Title
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
AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy L. Du Zhikun Zhang Shaojie Bai Changchang Liu S. Ji Peng Cheng Jiming Chen 96 36 0 14 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
The Connection between Out-of-Distribution Generalization and Privacy of ML Models Divyat Mahajan Shruti Tople Amit Sharma OOD 21 7 0 07 Oct 2021
Task-aware Privacy Preservation for Multi-dimensional Data Jiangnan Cheng A. Tang Sandeep P. Chinchali 31 7 0 05 Oct 2021
Towards General-purpose Infrastructure for Protecting Scientific Data Under Study Andrew Trask Kritika Prakash 46 3 0 04 Oct 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
Releasing Graph Neural Networks with Differential Privacy Guarantees Iyiola E. Olatunji Thorben Funke Megha Khosla 37 45 0 18 Sep 2021
Privacy-Preserving Machine Learning: Methods, Challenges and Directions Runhua Xu Nathalie Baracaldo J. Joshi 32 100 0 10 Aug 2021
DarkGAN: Exploiting Knowledge Distillation for Comprehensible Audio Synthesis with GANs J. Nistal Stefan Lattner G. Richard 26 8 0 03 Aug 2021
DTGAN: Differential Private Training for Tabular GANs A. Kunar Robert Birke Zilong Zhao L. Chen 30 11 0 06 Jul 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
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
Antipodes of Label Differential Privacy: PATE and ALIBI Mani Malek Ilya Mironov Karthik Prasad I. Shilov Florian Tramèr 16 62 0 07 Jun 2021
Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI Mohit Kumar Bernhard A. Moser Lukas Fischer B. Freudenthaler 18 1 0 06 Jun 2021
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs Mohammad Malekzadeh Anastasia Borovykh Deniz Gündüz MIACV 24 42 0 25 May 2021
Differentially Private Federated Knowledge Graphs Embedding Hao Peng Haoran Li Yangqiu Song V. Zheng Jianxin Li FedML 36 80 0 17 May 2021
Knowledge Distillation as Semiparametric Inference Tri Dao G. Kamath Vasilis Syrgkanis Lester W. Mackey 40 31 0 20 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
Membership Inference Attacks on Machine Learning: A Survey Hongsheng Hu Z. Salcic Lichao Sun Gillian Dobbie Philip S. Yu Xuyun Zhang MIACV 35 412 0 14 Mar 2021
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning Shaoxiong Ji Yue Tan Teemu Saravirta Zhiqin Yang Yixin Liu Lauri Vasankari Shirui Pan Guodong Long A. Walid FedML 37 76 0 25 Feb 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 94 111 0 25 Feb 2021
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting Ismat Jarin Birhanu Eshete 26 18 0 19 Feb 2021
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up? R. Guerraoui Nirupam Gupta Rafael Pinot Sébastien Rouault John Stephan 33 30 0 16 Feb 2021
Deep Learning with Label Differential Privacy Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Chiyuan Zhang 42 146 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 73 57 0 09 Feb 2021
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models Yugeng Liu Rui Wen Xinlei He A. Salem Zhikun Zhang Michael Backes Emiliano De Cristofaro Mario Fritz Yang Zhang AAML 17 125 0 04 Feb 2021
Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary Jean-Francois Rajotte Soumendu Sundar Mukherjee Caleb Robinson Anthony Ortiz Christopher West J. L. Ferres R. Ng FedML MedIm 130 40 0 18 Jan 2021
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
TransMIA: Membership Inference Attacks Using Transfer Shadow Training Seira Hidano Takao Murakami Yusuke Kawamoto MIACV 27 13 0 30 Nov 2020
Differentially Private Synthetic Data: Applied Evaluations and Enhancements Lucas Rosenblatt Xiao-Yang Liu Samira Pouyanfar Eduardo de Leon Anuj M. Desai Joshua Allen SyDa 26 64 0 11 Nov 2020
Practical One-Shot Federated Learning for Cross-Silo Setting Qinbin Li Bingsheng He D. Song FedML 18 114 0 02 Oct 2020
Private data sharing between decentralized users through the privGAN architecture Jean-Francois Rajotte R. Ng FedML 26 3 0 14 Sep 2020
Federated Model Distillation with Noise-Free Differential Privacy Lichao Sun Lingjuan Lyu FedML 29 106 0 11 Sep 2020
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 33 51 0 03 Sep 2020
NoPeek: Information leakage reduction to share activations in distributed deep learning Praneeth Vepakomma Abhishek Singh O. Gupta Ramesh Raskar MIACV FedML 24 84 0 20 Aug 2020
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data Sohei Itahara Takayuki Nishio Yusuke Koda M. Morikura Koji Yamamoto FedML 25 251 0 14 Aug 2020
Synthesizing Property & Casualty Ratemaking Datasets using Generative Adversarial Networks Marie-Pier Côté Brian Hartman Olivier Mercier Joshua Meyers Jared Cummings Elijah Harmon GAN SyDa 33 8 0 13 Aug 2020
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence Tianqing Zhu Dayong Ye Wei Wang Wanlei Zhou Philip S. Yu SyDa 38 125 0 05 Aug 2020
Anonymizing Machine Learning Models Abigail Goldsteen Gilad Ezov Ron Shmelkin Micha Moffie Ariel Farkash MIACV 16 5 0 26 Jul 2020
Quality Inference in Federated Learning with Secure Aggregation Balázs Pejó G. Biczók FedML 21 22 0 13 Jul 2020
The Trade-Offs of Private Prediction L. V. D. van der Maaten Awni Y. Hannun 25 22 0 09 Jul 2020
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification Yingxue Zhou Zhiwei Steven Wu A. Banerjee 24 108 0 07 Jul 2020
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning Seth Neel Aaron Roth Saeed Sharifi-Malvajerdi MU 17 250 0 06 Jul 2020
SPEED: Secure, PrivatE, and Efficient Deep learning Arnaud Grivet Sébert Rafael Pinot Martin Zuber Cédric Gouy-Pailler Renaud Sirdey FedML 15 20 0 16 Jun 2020
Knowledge Distillation: A Survey Jianping Gou B. Yu Stephen J. Maybank Dacheng Tao VLM 23 2,851 0 09 Jun 2020
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins? Jeremy Georges-Filteau Elisa Cirillo SyDa AI4CE 36 17 0 27 May 2020
An Overview of Privacy in Machine Learning Emiliano De Cristofaro SILM 30 83 0 18 May 2020
Private Stochastic Convex Optimization: Optimal Rates in Linear Time Vitaly Feldman Tomer Koren Kunal Talwar 13 203 0 10 May 2020