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A basic problem in the design of privacy-preserving algorithms is the private maximization problem: the goal is to pick an item from a universe that (approximately) maximizes a data-dependent function, all under the constraint of differential privacy.
Computable shell decomposition bounds
John Langford and David McAllester · 2004
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Practical privacy: the SuLQ framework
A. Blum, C. Dwork, F. McSherry, and K. Nissim · 2005
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.
Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
Differential privacy: A survey of results
Cynthia Dwork · 2008
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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Bounds on the sample complexity for private learning and private data release
Amos Beimel, Shiva Prasad Kasiviswanathan, and Kobbi Nissim · 2010
Earlier work this paper cites.
Discovering frequent patterns in sensitive data
Raghav Bhaskar, Srivatsan Laxman, Adam Smith, and Abhradeep Thakurta · 2010
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Data mining with differential privacy
A. Friedman and A. Schuster · 2010
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A multiplicative weights mechanism for privacy-preserving data analysis
Moritz Hardt and Guy N Rothblum · 2010
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On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
Earlier work this paper cites.
A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
Cited alongside, same era.
Sample complexity bounds for differentially private learning
Kamalika Chaudhuri and Daniel Hsu · 2011
Cited alongside, same era.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
Cited alongside, same era.
Publishing set-valued data via differential privacy
Rui Chen, Noman Mohammed, Benjamin CM Fung, Bipin C Desai, and Li Xiong · 2011
Cited alongside, same era.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
Cited alongside, same era.
Privacy-preserving statistical estimation with optimal convergence rates
Adam Smith · 2011
Cited alongside, same era.
Privbasis: frequent itemset mining with differential privacy
Ninghui Li, Wahbeh Qardaji, Dong Su, and Jianneng Cao · 2012
Later among the works it cites.
Privacy-preserving data sharing for genome-wide association studies
Caroline Uhler, Aleksandra B. Slavkovic, and Stephen E. Fienberg · 2012
Later among the works it cites.
On differentially private frequent itemset mining
Chen Zeng, Jeffrey F Naughton, and Jin-Yi Cai · 2012
Later among the works it cites.
A learning theory approach to noninteractive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2013
Later among the works it cites.
Mining frequent patterns with differential privacy
Luca Bonomi and Li Xiong · 2013
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A stability-based validation procedure for differentially private machine learning
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Convergence rates for differentially private statistical estimation
Kamalika Chaudhuri and Daniel Hsu · 2012
Cited alongside, same era.
Near-optimal differentially private principal components
Kamalika Chaudhuri, Anand D. Sarwate, and Kaushik Sinha · 2012
Cited alongside, same era.
Lower bounds in differential privacy
Anindya De · 2012
Cited alongside, same era.
Differentially private online learning
Prateek Jain, Pravesh Kothari, and Abhradeep Thakurta · 2012
Cited alongside, same era.
Private learning and sanitization: Pure vs. approximate differential privacy
Amos Beimel, Kobbi Nissim, and Uri Stemmer
Cited in the paper.
Characterizing the sample complexity of private learners
Amos Beimel, Kobbi Nissim, and Uri Stemmer
Cited in the paper.
Kamalika Chaudhuri and Staal A Vinterbo · 2013
Later among the works it cites.
Signal processing and machine learning with differential privacy: Algorithms and challenges for continuous data
A.D. Sarwate and K. Chaudhuri · 2013
Later among the works it cites.
Differentially private feature selection via stability arguments, and the robustness of the lasso
Adam Smith and Abhradeep Thakurta · 2013
Later among the works it cites.
Private empirical risk minimization, revisited
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Closest in time.
Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2014
Closest in time.