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A central problem in differentially private data analysis is how to design efficient algorithms capable of answering large numbers of counting queries on a sensitive database.
Tracing traitors
Benny Chor, Amos Fiat, and Moni Naor · 1994
Earlier work this paper cites.
Collusion-secure fingerprinting for digital data
Dan Boneh and James Shaw · 1998
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On crafty pirates and foxy tracers
Aggelos Kiayias and Moti Yung · 2001
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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Practical privacy: the sulq framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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Fully collusion resistant traitor tracing with short ciphertexts and private keys
Dan Boneh, Amit Sahai, and Brent Waters · 2006
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.
A learning theory approach to non-interactive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2008
Cited alongside, same era.
Optimal probabilistic fingerprint codes
Gábor Tardos · 2008
Cited alongside, same era.
On the complexity of differentially private data release: efficient algorithms and hardness results
Cynthia Dwork, Moni Naor, Omer Reingold, Guy N. Rothblum, and Salil P. Vadhan · 2009
Cited alongside, same era.
Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 2010
Cited alongside, same era.
A multiplicative weights mechanism for privacy-preserving data analysis
Moritz Hardt and Guy N. Rothblum · 2010
Cited alongside, same era.
Interactive privacy via the median mechanism
Aaron Roth and Tim Roughgarden · 2010
Cited alongside, same era.
PCPs and the hardness of generating private synthetic data
Jonathan Ullman and Salil P. Vadhan · 2011
Later among the works it cites.
Pseudorandom generators with long stretch and low locality from random local one-way functions
Benny Applebaum · 2012
Closest in time.
The privacy of the analyst and the power of the state
Cynthia Dwork, Moni Naor, and Salil P. Vadhan · 2012
Closest in time.
Iterative constructions and private data release
Anupam Gupta, Aaron Roth, and Jonathan Ullman · 2012
Closest in time.
A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank McSherry · 2012
Closest in time.
Private data release via learning thresholds
Moritz Hardt, Guy N. Rothblum, and Rocco A. Servedio · 2012
Closest in time.
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Privately releasing conjunctions and the statistical query barrier
Anupam Gupta, Moritz Hardt, Aaron Roth, and Jonathan Ullman · 2011
Cited alongside, same era.
Faster algorithms for privately releasing marginals
Justin Thaler, Jonathan Ullman, and Salil P. Vadhan · 2012
Closest in time.