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In Semi-Supervised Semi-Private (SP) learning, the learner has access to both public unlabelled and private labelled data.
Extensions of lipschitz mappings into hilbert space
William B. Johnson · 1984
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Neural Network Learning: Theoretical Foundations
Martin Anthony and Peter L. Bartlett · 1999
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Practical privacy: the SuLQ framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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Privacy practices of internet users: Self-reports versus observed behavior
Carlos Jensen, Colin Potts, and Christian Jensen · 2005
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On the convergence of eigenspaces in kernel principal component analysis
Laurent Zwald and Gilles Blanchard · 2005
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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A firm foundation for private data analysis
Cynthia Dwork · 2011
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Fast private data release algorithms for sparse queries
Avrim Blum and Aaron Roth · 2013
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Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel · 2013
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Sample complexity bounds on differentially private learning via communication complexity
Vitaly Feldman and David Xiao · 2014
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
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Rényi differential privacy
Ilya Mironov · 2017
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Low rank approximation
Shayan Oveis Gharan · 2017
Cited alongside, same era.
Scalable private learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Ulfar Erlingsson · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
Identifying medical diagnoses and treatable diseases by image-based deep learning
Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina CS Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, et al · 2018
Cited alongside, same era.
Rotation equivariant CNNs for digital pathology
Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
Cited alongside, same era.
Differentially private learning needs better features (or much more data)
Florian Tramer and Dan Boneh · 2021
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Do not let privacy overbill utility: Gradient embedding perturbation for private learning
Da Yu, Huishuai Zhang, Wei Chen, and Tie-Yan Liu · 2021
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Bypassing the ambient dimension: Private SGD with gradient subspace identification
Yingxue Zhou, Steven Wu, and Arindam Banerjee · 2021
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SGD with low-dimensional gradients with applications to private and distributed learning
Shiva Prasad Kasiviswanathan · 2021
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Adversary instantiation: Lower bounds for differentially private machine learning
Milad Nasr, Shuang Songi, Abhradeep Thakurta, Nicolas Papemoti, and Nicholas Carlin · 2021
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https://tinyurl.com/2p8vpsp2 , 2021
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Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2018
Cited alongside, same era.
High-Dimensional Probability: An Introduction with Applications in Data Science
Roman Vershynin · 2018
Cited alongside, same era.
Limits of private learning with access to public data
Noga Alon, Raef Bassily, and Shay Moran · 2019
Cited alongside, same era.
Billion-scale semi-supervised learning for image classification
I. Zeki Yalniz, Hervé Jégou, Kan Chen, Manohar Paluri, and Dhruv Mahajan · 2019
Cited alongside, same era.
https://www.kaggle.com/datasets/shubhamgoel27/dermnet , 2019
Dataset for 23 skin lesions · 2019
Cited alongside, same era.
Pytorch image models
Ross Wightman · 2019
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Flowers dataset · 2021
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Opacus: User-friendly differential privacy library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, and Ilya Mironov · 2021
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Enhanced membership inference attacks against machine learning models
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, Vincent Bindschaedler, and Reza Shokri · 2022
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Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasar, Shuang Song, Andreas Terzis, and Florian Tramèr · 2022
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Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
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Toward training at ImageNet scale with differential privacy
Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, and Abhradeep Thakurta · 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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solo-learn: A library of self-supervised methods for visual representation learning
Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, and Elisa Ricci · 2022
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Personalized PATE: Differential privacy for machine learning with individual privacy guarantees
Christopher Mühl and Franziska Boenisch · 2022
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How unfair is private learning?
Amartya Sanyal, Yaxi Hu, and Fanny Yang · 2022
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Choosing public datasets for private machine learning via gradient subspace distance
Xin Gu, Gautam Kamath, and Zhiwei Steven Wu · 2023
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Sample-efficient private data release for lipschitz functions under sparsity assumptions
Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hönig, and Fanny Yang · 2023
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How robust are pre-trained models to distribution shift?
Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H.S. Torr, and Amartya Sanyal · 2023
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Have it your way: Individualized privacy assignment for DP-SGD
Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, and Nicolas Papernot · 2023
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