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We consider the problem of designing scalable, robust protocols for computing statistics about sensitive data.
Randomized response: A survey technique for eliminating evasive answer bias
S. L. Warner · 1965
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Untraceable electronic mail, return addresses, and digital pseudonyms
D. L. Chaum · 1981
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Efficient noise-tolerant learning from statistical queries
M. J. Kearns · 1993
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Limiting privacy breaches in privacy preserving data mining
A. Evfimievski, J. Gehrke, and R. Srikant · 2003
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Mechanism design via differential privacy
F. McSherry and K. Talwar · 2007
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Distributed private data analysis: Simultaneously solving how and what
A. Beimel, K. Nissim, and E. Omri · 2008
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On the ‘semantics’ of differential privacy: A bayesian formulation
S. P. Kasiviswanathan and A. Smith · 2008
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What can we learn privately?
S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith · 2008
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Differential privacy and the secrecy of the sample, 2009
A. Smith · 2009
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Boosting and differential privacy
C. Dwork, G. N. Rothblum, and S. P. Vadhan · 2010
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Privacy-preserving aggregation of time-series data
E. Shi, T. H. Chan, E. G. Rieffel, R. Chow, and D. Song · 2011
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Optimal lower bound for differentially private multi-party aggregation
T. H. Chan, E. Shi, and D. Song · 2012
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Local privacy and statistical minimax rates
J. C. Duchi, M. I. Jordan, and M. J. Wainwright · 2013
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RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Ú. Erlingsson, V. Pihur, and A. Korolova · 2014
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Local, private, efficient protocols for succinct histograms
R. Bassily and A. Smith · 2015
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Vuvuzela: Scalable private messaging resistant to traffic analysis
J. van den Hooff, D. Lazar, M. Zaharia, and N. Zeldovich · 2015
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Riffle: An efficient communication system with strong anonymity
A. Kwon, D. Lazar, S. Devadas, and B. Ford · 2016
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Practical secure aggregation for privacy preserving machine learning
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 2017
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Prio: Private, robust, and scalable computation of aggregate statistics
H. Corrigan-Gibbs and D. Boneh · 2017
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Tight lower bounds for differentially private selection
T. Steinke and J. Ullman · 2017
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Learning new words, May 9 2017
A. G. Thakurta, A. H. Vyrros, U. S. Vaishampayan, G. Kapoor, J. Freudiger, V. R. Sridhar, and D. Davidson · 2017
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Stadium: A distributed metadata-private messaging system
N. Tyagi, Y. Gilad, D. Leung, M. Zaharia, and N. Zeldovich · 2017
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Apple tries to peek at user habits without violating privacy
R. McMillan · 2016
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The complexity of differential privacy
S. Vadhan · 2016
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The price of selection in differential privacy
M. Bafna and J. Ullman · 2017
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PROCHLO: Strong privacy for analytics in the crowd
A. Bittau, U. Erlingsson, P. Maniatis, I. Mironov, A. Raghunathan, D. Lie, M. Rudominer, U. Kode, J. Tinnes, and B. Seefeld · 2017
Cited alongside, same era.
Privacy-preserving stream aggregation with fault tolerance
T.-H. H. Chan, E. Shi, and D. Song
Cited in the paper.
Our data, ourselves: Privacy via distributed noise generation
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor
Cited in the paper.
M. Zhilyaev and D. Zeber · 2017
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The U.S. Census Bureau adopts differential privacy
J. M. Abowd · 2018
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Heavy hitters and the structure of local privacy
M. Bun, J. Nelson, and U. Stemmer · 2018
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Tight lower bounds for locally differentially private selection
J. Ullman · 2018
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Amplification by shuffling: From local to central differential privacy by anonymity
U. Erlingsson, V. Feldman, I. Mironov, A. Raghunathan, K. Talwar, and A. Thakurta · 2019
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