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Most works in learning with differential privacy (DP) have focused on the setting where each user has a single sample.
Randomized response: A survey technique for eliminating evasive answer bias
Stanley L Warner · 1965
Earlier work this paper cites.
A theory of the learnable
Leslie G. Valiant · 1984
Earlier work this paper cites.
Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
Nick Littlestone · 1987
Earlier work this paper cites.
Learning decision lists
Ronald L. Rivest · 1987
Earlier work this paper cites.
Weakly learning DNF and characterizing statistical query learning using Fourier analysis
Avrim Blum, Merrick L. Furst, Jeffrey C. Jackson, Michael J. Kearns, Yishay Mansour, and Steven Rudich · 1994
Earlier work this paper cites.
On the resemblance and containment of documents
Andrei Z. Broder · 1997
Earlier work this paper cites.
Efficient noise-tolerant learning from statistical queries
Michael J. Kearns · 1998
Earlier work this paper cites.
Similarity estimation techniques from rounding algorithms
Moses S Charikar · 2002
Earlier work this paper cites.
Approximation algorithms for classification problems with pairwise relationships: metric labeling and Markov random fields
Jon M. Kleinberg and Éva Tardos · 2002
Earlier work this paper cites.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith · 2006
Earlier work this paper cites.
Parallel repetition: simplifications and the no-signaling case
Thomas Holenstein · 2007
Earlier work this paper cites.
Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
A simple polynomial-time rescaling algorithm for solving linear programs
John Dunagan and Santosh S. Vempala · 2008
Earlier work this paper cites.
Releasing search queries and clicks privately
Aleksandra Korolova, Krishnaram Kenthapadi, Nina Mishra, and Alexandros Ntoulas · 2009
Earlier work this paper cites.
On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
Earlier work this paper cites.
Local privacy and statistical minimax rates
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2013
Earlier work this paper cites.
Analyzing graphs with node differential privacy
Shiva Prasad Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
Cited alongside, same era.
How Google tricks itself to protect Chrome user privacy
Stephen Shankland · 2014
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Apple’s “differential privacy” is about collecting your data – but not your data
Andy Greenberg · 2016
Cited alongside, same era.
Learning with privacy at scale
Apple Differential Privacy Team · 2017
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
Later among the works it cites.
Beyond inferring class representatives: User-level privacy leakage from federated learning
Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi · 2019
Later among the works it cites.
Private summation in the multi-message shuffle model
Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim · 2020
Later among the works it cites.
Separating local & shuffled differential privacy via histograms
Victor Balcer and Albert Cheu · 2020
Later among the works it cites.
An equivalence between private classification and online prediction
Mark Bun, Roi Livni, and Shay Moran · 2020
Later among the works it cites.
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Prochlo: Strong privacy for analytics in the crowd
Andrea Bittau, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnés, and Bernhard Seefeld · 2017
Cited alongside, same era.
Practical locally private heavy hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, and Abhradeep Guha Thakurta · 2017
Cited alongside, same era.
Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
Cited alongside, same era.
The complexity of differential privacy
Salil P. Vadhan · 2017
Cited alongside, same era.
The US Census Bureau adopts differential privacy
John M Abowd · 2018
Cited alongside, same era.
Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Cited alongside, same era.
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, and Abhradeep Thakurta · 2020
Later among the works it cites.
Smoothly bounding user contributions in differential privacy
Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, and Lijie Ren · 2020
Later among the works it cites.
Pure differentially private summation from anonymous messages
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, and Ameya Velingker · 2020
Later among the works it cites.
Private counting from anonymous messages: Near-optimal accuracy with vanishing communication overhead
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, and Rasmus Pagh · 2020
Later among the works it cites.
Private aggregation from fewer anonymous messages
Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, and Ameya Velingker · 2020
Later among the works it cites.
Learning discrete distributions: user vs item-level privacy
Yuhan Liu, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, and Michael Riley · 2020
Later among the works it cites.
Differentially private SQL with bounded user contribution
Royce J Wilson, Celia Yuxin Zhang, William Lam, Damien Desfontaines, Daniel Simmons-Marengo, and Bryant Gipson · 2020
Later among the works it cites.
Differentially private histograms in the shuffle model from fake users
Albert Cheu and Maxim Zhilyaev · 2021
Closest in time.
On the power of multiple anonymous messages: Frequency estimation and selection in the shuffle model of differential privacy
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, and Ameya Velingker · 2021
Closest in time.
Sample-efficient proper PAC learning with approximate differential privacy
Badih Ghazi, Noah Golowich, Ravi Kumar, and Pasin Manurangsi · 2021
Closest in time.
Differentially private aggregation in the shuffle model: Almost central accuracy in almost a single message
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, and Amer Sinha · 2021
Closest in time.
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, and Rasmus Pagh · 2021
Closest in time.
Differentially private nonparametric regression under a growth condition
Noah Golowich · 2021
Closest in time.
Reproducibility in learning
Russell Impagliazzo, Rex Lei, Toniann Pitassi, and Jessica Sorrell · 2021
Closest in time.
Learning with user-level privacy
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, and Ananda Theertha Suresh · 2021
Closest in time.