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The shuffle model of local differential privacy is an advanced method of privacy amplification designed to enhance privacy protection with high utility.
The accuracy of the Gaussian approximation to the sum of independent variates
Berry, A. C. 1941 · 1941
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The accuracy of the Gaussian approximation to the sum of independent variates
Berry, A. C. 1941 · 1941
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On the Liapunov limit error in the theory of probability
Esseen, C. G. 1942 · 1942
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On the Liapunov limit error in the theory of probability
Esseen, C. G. 1942 · 1942
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Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
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Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
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Privacy and rationality in individual decision making
Acquisti, A.; and Grossklags, J. 2005 · 2005
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Privacy and rationality in individual decision making
Acquisti, A.; and Grossklags, J. 2005 · 2005
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What Can We Learn Privately?
Kasiviswanathan, S. P.; Lee, H. K.; Nissim, K.; Raskhodnikova, S.; and Smith, A. 2011 · 2011
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What Can We Learn Privately?
Kasiviswanathan, S. P.; Lee, H. K.; Nissim, K.; Raskhodnikova, S.; and Smith, A. 2011 · 2011
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The algorithmic foundations of differential privacy
Dwork, C.; and Roth, A. 2014 · 2014
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The algorithmic foundations of differential privacy
Dwork, C.; and Roth, A. 2014 · 2014
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Conservative or liberal? Personalized differential privacy
Jorgensen, Z.; Yu, T.; and Cormode, G. 2015 · 2015
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The composition theorem for differential privacy
Kairouz, P.; Oh, S.; and Viswanath, P. 2015 · 2015
Cited alongside, same era.
Conservative or liberal? Personalized differential privacy
Jorgensen, Z.; Yu, T.; and Cormode, G. 2015 · 2015
Cited alongside, same era.
The composition theorem for differential privacy
Kairouz, P.; Oh, S.; and Viswanath, P. 2015 · 2015
Cited alongside, same era.
Deep learning with differential privacy
Abadi, M.; Chu, A.; Goodfellow, I.; McMahan, H. B.; Mironov, I.; Talwar, K.; and Zhang, L. 2016 · 2016
Cited alongside, same era.
Deep learning with differential privacy
Abadi, M.; Chu, A.; Goodfellow, I.; McMahan, H. B.; Mironov, I.; Talwar, K.; and Zhang, L. 2016 · 2016
Cited alongside, same era.
Prochlo: Strong privacy for analytics in the crowd
Bittau, A.; Erlingsson, Ú.; Maniatis, P.; Mironov, I.; Raghunathan, A.; Lie, D.; Rudominer, M.; Kode, U.; Tinnes, J.; and Seefeld, B. 2017 · 2017
Cited alongside, same era.
Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Feldman, V.; McMillan, A.; and Talwar, K. 2022 · 2021
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On the Renyi differential privacy of the shuffle model
Girgis, A. M.; Data, D.; Diggavi, S.; Suresh, A. T.; and Kairouz, P. 2021b · 2021
Later among the works it cites.
Flame: Differentially private federated learning in the shuffle model
Liu, R.; Cao, Y.; Chen, H.; Guo, R.; and Yoshikawa, M. 2021 · 2021
Later among the works it cites.
AdaPDP: Adaptive Personalized Differential Privacy
Niu, B.; Chen, Y.; Wang, B.; Wang, Z.; Li, F.; and Cao, J. 2021 · 2021
Later among the works it cites.
Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Feldman, V.; McMillan, A.; and Talwar, K. 2022 · 2021
Later among the works it cites.
On the Renyi differential privacy of the shuffle model
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Prochlo: Strong privacy for analytics in the crowd
Bittau, A.; Erlingsson, Ú.; Maniatis, P.; Mironov, I.; Raghunathan, A.; Lie, D.; Rudominer, M.; Kode, U.; Tinnes, J.; and Seefeld, B. 2017 · 2017
Cited alongside, same era.
The privacy blanket of the shuffle model
Balle, B.; Bell, J.; Gascón, A.; and Nissim, K. 2019 · 2019
Cited alongside, same era.
Distributed differential privacy via shuffling
Cheu, A.; Smith, A.; Ullman, J.; Zeber, D.; and Zhilyaev, M. 2019 · 2019
Cited alongside, same era.
Amplification by shuffling: From local to central differential privacy via anonymity
Erlingsson, Ú.; Feldman, V.; Mironov, I.; Raghunathan, A.; Talwar, K.; and Thakurta, A. 2019 · 2019
Cited alongside, same era.
The privacy blanket of the shuffle model
Balle, B.; Bell, J.; Gascón, A.; and Nissim, K. 2019 · 2019
Cited alongside, same era.
Distributed differential privacy via shuffling
Cheu, A.; Smith, A.; Ullman, J.; Zeber, D.; and Zhilyaev, M. 2019 · 2019
Cited alongside, same era.
Girgis, A. M.; Data, D.; Diggavi, S.; Suresh, A. T.; and Kairouz, P. 2021b · 2021
Later among the works it cites.
Flame: Differentially private federated learning in the shuffle model
Liu, R.; Cao, Y.; Chen, H.; Guo, R.; and Yoshikawa, M. 2021 · 2021
Later among the works it cites.
AdaPDP: Adaptive Personalized Differential Privacy
Niu, B.; Chen, Y.; Wang, B.; Wang, Z.; Li, F.; and Cao, J. 2021 · 2021
Later among the works it cites.
Gaussian differential privacy
Dong, J.; Roth, A.; and Su, W. J. 2022 · 2022
Later among the works it cites.
Gaussian differential privacy
Dong, J.; Roth, A.; and Su, W. J. 2022 · 2022
Later among the works it cites.
Echo: Privacy amplification for Personalized Private Federated Learning with Shuffle Model
Liu, Y.; Zhao, S.; Li, X.; Liu, Y.; and Chen, H. 2023 · 2023
Closest in time.
Echo: Privacy amplification for Personalized Private Federated Learning with Shuffle Model
Liu, Y.; Zhao, S.; Li, X.; Liu, Y.; and Chen, H. 2023 · 2023
Closest in time.