Fetching the paper…
Reading the bibliography…
Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
Earlier work this paper cites.
Private convex empirical risk minimization and high-dimensional regression
Daniel Kifer, Adam Smith, and Abhradeep Thakurta · 2012
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Earlier work this paper cites.
Conservative or liberal? personalized differential privacy
Zach Jorgensen, Ting Yu, and Graham Cormode · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Accuracy first: Selecting a differential privacy level for accuracy constrained erm
Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, and Steven Z Wu · 2017
Earlier work this paper cites.
Rényi differential privacy
Ilya Mironov · 2017
Earlier work this paper cites.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Age progression/regression by conditional adversarial autoencoder
Zhifei Zhang, Yang Song, and Hairong Qi · 2017
Earlier work this paper cites.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Earlier work this paper cites.
Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
Earlier work this paper cites.
Approximate and probabilistic differential privacy definitions
Sebastian Meiser · 2018
Earlier work this paper cites.
Membership inference attack against differentially private deep learning model
Md Atiqur Rahman, Tanzila Rahman, Robert Laganiere, Noman Mohammed, and Yang Wang · 2018
Earlier work this paper cites.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Earlier work this paper cites.
Assessing differentially private deep learning with membership inference
Daniel Bernau, Philip-William Grassal, Jonas Robl, and Florian Kerschbaum · 2019
Cited alongside, same era.
Lucas Bourtoule, Varun Chandrasekaran, Christopher Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2019
Cited alongside, same era.
Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
Cited alongside, same era.
Rényi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang · 2019
Cited alongside, same era.
White-box vs black-box: Bayes optimal strategies for membership inference
Alexandre Sablayrolles, Matthijs Douze, Yann Ollivier, Cordelia Schmid, and Hervé Jégou · 2019
Evading the curse of dimensionality in unconstrained private glms
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta · 2021
Later among the works it cites.
Chasing your long tails: Differentially private prediction in health care settings
Vinith M Suriyakumar, Nicolas Papernot, Anna Goldenberg, and Marzyeh Ghassemi · 2021
Later among the works it cites.
Large scale private learning via low-rank reparametrization
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu · 2021
Later among the works it cites.
Scalable and efficient training of large convolutional neural networks with differential privacy
Zhiqi Bu, Jialin Mao, and Shiyun Xu · 2022
Closest in time.
The privacy onion effect: Memorization is relative
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, and Florian Tramer · 2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Per-instance differential privacy
Yu-Xiang Wang · 2019
Cited alongside, same era.
Subsampled rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan · 2019
Cited alongside, same era.
Understanding gradient clipping in private sgd: A geometric perspective
Xiangyi Chen, Steven Z Wu, and Mingyi Hong · 2020
Cited alongside, same era.
What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
Cited alongside, same era.
Auditing differentially private machine learning: How private is private sgd?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea · 2020
Cited alongside, same era.
Tempered sigmoid activations for deep learning with differential privacy
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, and Ulfar Erlingsson · 2020
Cited alongside, same era.
Private-knn: Practical differential privacy for computer vision
Yuqing Zhu, Xiang Yu, Manmohan Chandraker, and Yu-Xiang Wang · 2020
Cited alongside, same era.
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
Closest in time.
Mixed differential privacy in computer vision
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, and Stefano Soatto · 2022
Closest in time.
The impact of differential privacy on group disparity mitigation
Victor Petrén Bach Hansen, Atula Tejaswi Neerkaje, Ramit Sawhney, Lucie Flek, and Anders Søgaard · 2022
Closest in time.
Individual privacy accounting with gaussian differential privacy
Antti Koskela, Marlon Tobaben, and Antti Honkela · 2022
Closest in time.
Large language models can be strong differentially private learners
Xuechen Li, Florian Tramèr, Percy Liang, and Tatsunori Hashimoto · 2022
Closest in time.
Stochastic differentially private and fair learning
Andrew Lowy, Devansh Gupta, and Meisam Razaviyayn · 2022
Closest in time.
Personalized PATE: Differential privacy for machine learning with individual privacy guarantees
Christopher Mühl and Franziska Boenisch · 2022
Closest in time.
Exploring the unfairness of dp-sgd across settings
Frederik Noe, Rasmus Herskind, and Anders Søgaard · 2022
Closest in time.
Fully adaptive composition in differential privacy
Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, and Zhiwei Steven Wu · 2022
Closest in time.
Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, and Huishuai Zhang · 2022
Closest in time.
Have it your way: Individualized privacy assignment for dp-sgd
Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, and Nicolas Papernot · 2023
Closest in time.
Stronger privacy amplification by shuffling for rényi and approximate differential privacy
Vitaly Feldman, Audra McMillan, and Kunal Talwar · 2023
Closest in time.
Numerical accounting in the shuffle model of differential privacy
Antti Koskela, Mikko A Heikkilä, and Antti Honkela · 2023
Closest in time.
Privacy amplification via shuffling: Unified, simplified, and tightened
Shaowei Wang · 2023
Closest in time.