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We study the problem of multi-task learning under user-level differential privacy, in which $n$ users contribute data to $m$ tasks, each involving a subset of users.
Addressing the curse of imbalanced training sets: One-sided selection
M. Kubat and S. Matwin · 1997
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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The price of privacy and the limits of lp decoding
C. Dwork, F. McSherry, and K. Talwar · 2007
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Boosting and differential privacy
C. Dwork, G. N. Rothblum, and S. Vadhan · 2010
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A multiplicative weights mechanism for privacy-preserving data analysis
M. Hardt and G. N. Rothblum · 2010
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Privacy violations using microtargeted ads: A case study
A. Korolova · 2010
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The million song dataset
T. Bertin-Mahieux, D. P. W. Ellis, B. Whitman, and P. Lamere · 2011
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J. A. Calandrino, A. Kilzer, A. Narayanan, E. W. Felten, and V. Shmatikov · 2011
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Challenging the long tail recommendation
H. Yin, B. Cui, J. Li, J. Yao, and C. Chen · 2012
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Concentration inequalities: A nonasymptotic theory of independence
S. Boucheron, G. Lugosi, and P. Massart · 2013
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Low-rank matrix completion using alternating minimization
P. Jain, P. Netrapalli, and S. Sanghavi · 2013
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Analyzing graphs with node differential privacy
S. P. Kasiviswanathan, K. Nissim, S. Raskhodnikova, and A. Smith · 2013
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Local low-rank matrix approximation
J. Lee, S. Kim, G. Lebanon, and Y. Singer · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
R. Bassily, A. Smith, and A. Thakurta · 2014
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The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
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Privately solving linear programs
J. Hsu, A. Roth, T. Roughgarden, and J. Ullman · 2014
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Mechanism design in large games: Incentives and privacy
M. Kearns, M. Pai, A. Roth, and J. Ullman · 2014
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Calibrating data to sensitivity in private data analysis: A platform for differentially-private analysis of weighted datasets
D. Proserpio, S. Goldberg, and F. McSherry · 2014
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Private multiplicative weights beyond linear queries
J. Ullman · 2015
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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On the theory and practice of privacy-preserving bayesian data analysis
J. Foulds, J. Geumlek, M. Welling, and K. Chaudhuri · 2016
Differential privacy has disparate impact on model accuracy
E. Bagdasaryan, O. Poursaeed, and V. Shmatikov · 2019
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Smoothly bounding user contributions in differential privacy
A. Epasto, M. Mahdian, J. Mao, V. Mirrokni, and L. Ren · 2020
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Differentially private meta-learning
J. Li, M. Khodak, S. Caldas, and A. Talwalkar · 2020
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Long-Tail Session-Based Recommendation , page 509–514
S. Liu and Y. Zheng · 2020
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Differentially private sql with bounded user contribution
R. J. Wilson, C. Y. Zhang, W. Lam, D. Desfontaines, D. Simmons-Marengo, and B. Gipson · 2020
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Private alternating least squares: Practical private matrix completion with tighter rates
S. Chien, P. Jain, W. Krichene, S. Rendle, S. Song, A. Thakurta, and L. Zhang · 2021
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The movielens datasets: History and context
F. M. Harper and J. A. Konstan · 2016
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Rényi differential privacy
I. Mironov · 2017
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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The u.s. census bureau adopts differential privacy
J. M. Abowd · 2018
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Differentially private matrix completion revisited
P. Jain, O. D. Thakkar, and A. Thakurta · 2018
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Variational autoencoders for collaborative filtering
D. Liang, R. G. Krishnan, M. D. Hoffman, and T. Jebara · 2018
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Individual privacy accounting via a rényi filter
V. Feldman and T. Zrnic · 2021
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Private multi-task learning: Formulation and applications to federated learning
S. Hu, Z. S. Wu, and V. Smith · 2021
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Differentially private model personalization
P. Jain, J. Rush, A. Smith, S. Song, and A. Guha Thakurta · 2021
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Learning with user-level privacy
D. Levy, Z. Sun, K. Amin, S. Kale, A. Kulesza, M. Mohri, and A. T. Suresh · 2021
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Linkedin’s audience engagements api: A privacy preserving data analytics system at scale
R. Rogers, S. Subramaniam, S. Peng, D. Durfee, S. Lee, S. K. Kancha, S. Sahay, and P. Ahammad · 2021
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Removing disparate impact on model accuracy in differentially private stochastic gradient descent
D. Xu, W. Du, and X. Wu · 2021
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Plume: Differential privacy at scale
K. Amin, J. Gillenwater, M. Joseph, A. Kulesza, and S. Vassilvitskii · 2022
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Mean estimation with user-level privacy under data heterogeneity
R. Cummings, V. Feldman, A. McMillan, and K. Talwar · 2022
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