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Differential Privacy (DP) image data synthesis, which leverages the DP technique to generate synthetic data to replace the sensitive data, allowing organizations to share and utilize synthetic images without privacy concerns.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and et al · 1998
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, and et al · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, and et al · 2011
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Practicing differential privacy in health care: A review
Fida Kamal Dankar and Khaled El Emam · 2013
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, and et al · 2014
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Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, and et al · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Microsoft COCO captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, and et al · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and et al · 2015
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Show and tell: A neural image caption generator
Oriol Vinyals, Alexander Toshev, Samy Bengio, and et al · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, and et al · 2015
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Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian J. Goodfellow, and et al · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and et al · 2016
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Wasserstein generative adversarial networks
Martín Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, and et al · 2017
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Ilya Mironov · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, and et al · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Semantic image inpainting with deep generative models
Raymond A. Yeh, Chen Chen, Teck-Yian Lim, and et al · 2017
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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, and et al · 2018
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Differentially private releasing via deep generative model
Xinyang Zhang, Shouling Ji, and Ting Wang · 2018
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Generative models for effective ML on private, decentralized datasets
Sean Augenstein, H. Brendan McMahan, Daniel Ramage, and et al · 2019
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From detection of individual metastases to classification of lymph node status at the patient level: The CAMELYON17 challenge
Péter Bándi, Oscar Geessink, Quirine Manson, and et al · 2019
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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PATE-GAN: generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela van der Schaar · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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PPGAN: privacy-preserving generative adversarial network
Yi Liu, Jialiang Peng, James Jian Qiao Yu, and et al · 2019
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Data poisoning against differentially-private learners: Attacks and defenses
Yuzhe Ma, Xiaojin Zhu, and Justin Hsu · 2019
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Rényi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
DP-CGAN: differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, and et al · 2020
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GS-WGAN: A gradient-sanitized approach for learning differentially private generators
Deep semantic segmentation of natural and medical images: a review
Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Paul Cohen, and et al · 2021
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See through gradients: Image batch recovery via gradinversion
Hongxu Yin, Arun Mallya, Arash Vahdat, and et al · 2021
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Privsyn: Differentially private data synthesis
Zhikun Zhang, Tianhao Wang, Ninghui Li, and et al · 2021
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Beit: BERT pre-training of image transformers
Hangbo Bao, Li Dong, Songhao Piao, and et al · 2022
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Differentially private diffusion models
Tim Dockhorn, Tianshi Cao, Arash Vahdat, and et al · 2022
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It’s raw! audio generation with state-space models
Karan Goel, Albert Gu, Chris Donahue, and et al · 2022
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Dingfan Chen, Tribhuvanesh Orekondy, and Mario Fritz · 2020
Cited alongside, same era.
Identification of epilepsy from intracranial eeg signals by using different neural network models
Chen Gong, Xiaoxiong Zhang, and Yunyun Niu · 2020
Cited alongside, same era.
Differential privacy in blockchain technology: A futuristic approach
Muneeb Ul Hassan, Mubashir Husain Rehmani, and Jinjun Chen · 2020
Cited alongside, same era.
Scaling laws for autoregressive generative modeling
Tom Henighan, Jared Kaplan, and et al · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
On the effectiveness of mitigating data poisoning attacks with gradient shaping
Sanghyun Hong, Varun Chandrasekaran, and et al · 2020
Cited alongside, same era.
Differential privacy and its applications in social network analysis: A survey
Honglu Jiang, Jian Pei, Dongxiao Yu, and et al · 2020
Cited alongside, same era.
Curiosity-driven and victim-aware adversarial policies
Chen Gong, Zhou Yang, Yunpeng Bai, and et al · 2022
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Pattern recognition of epilepsy using parallel probabilistic neural network
Chen Gong, Xingchen Zhou, and Yunyun Niu · 2022
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Dp 2 {}^{\mbox{2}} -vae: Differentially private pre-trained variational autoencoders
Dihong Jiang, Guojun Zhang, Mahdi Karami, and et al · 2022
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Toward training at imagenet scale with differential privacy
Alexey Kurakin, Steve Chien, Shuang Song, and et al · 2022
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Diffusion-lm improves controllable text generation
Xiang Li, John Thickstun, Ishaan Gulrajani, and et al · 2022
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramèr, Percy Liang, and et al · 2022
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PEARL: data synthesis via private embeddings and adversarial reconstruction learning
Seng Pei Liew, Tsubasa Takahashi, and Michihiko Ueno · 2022
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Bjarne Pfitzner and Bert Arnrich · 2022
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Hierarchical text-conditional image generation with CLIP latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, and et al · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, and et al · 2022
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Considerations for differentially private learning with large-scale public pretraining
Florian Tramèr, Gautam Kamath, and Nicholas Carlini · 2022
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, and et al · 2022
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A unified view of differentially private deep generative modeling
Dingfan Chen, Raouf Kerkouche, and Mario Fritz · 2023
Closest in time.
Differentially private diffusion models generate useful synthetic images
Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal, and et al · 2023
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Differentially private synthetic data generation via lipschitz-regularised variational autoencoders
Benedikt Groß and Gerhard Wunder · 2023
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Pre-trained perceptual features improve differentially private image generation
Frederik Harder, Milad Jalali, Danica J. Sutherland, and et al · 2023
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FFCV: Accelerating training by removing data bottlenecks
Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, and et al · 2023
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Differentially private synthetic data via foundation model apis 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, and et al · 2023
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Differentially private latent diffusion models
Saiyue Lyu, Margarita Vinaroz, Michael F. Liu, and et al · 2023
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Cross-entropy loss functions: Theoretical analysis and applications
Anqi Mao, Mehryar Mohri, and Yutao Zhong · 2023
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Membership inference attacks against diffusion models
Tomoya Matsumoto, Takayuki Miura, and Naoto Yanai · 2023
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OpenAI · 2023
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White-box membership inference attacks against diffusion models, 2023
Yan Pang, Tianhao Wang, Xuhui Kang, Mengdi Huai, and Yang Zhang · 2023
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