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The sensitivity metric in differential privacy, which is informally defined as the largest marginal change in output between neighboring databases, is of substantial significance in determining the accuracy of private data analyses.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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A learning theory approach to noninteractive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2008
Earlier work this paper cites.
Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John V. Pearson, Dietrich A. Stephan, Stanley F. Nelson, and David W. Craig · 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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Robust de-anonymization of large sparse datasets
Arvind Narayanan and Vitaly Shmatikov · 2008
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
Earlier work this paper cites.
Selling privacy at auction
Arpita Ghosh and Aaron Roth · 2011
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Approximately optimal auctions for selling privacy when costs are correlated with data
Lisa Fleischer and Yu-Han Lyu · 2012
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Take it or leave it: Running a survey when privacy comes at a cost
Katrina Ligett and Aaron Roth · 2012
Earlier work this paper cites.
Privacy-aware mechanism design
Kobbi Nissim, Claudio Orlandi, and Rann Smorodinsky · 2012
Cited alongside, same era.
Approximately optimal mechanism design via differential privacy
Kobbi Nissim, Rann Smorodinsky, and Moshe Tennenholtz · 2012
Cited alongside, same era.
Differentially private data analysis of social networks via restricted sensitivity
Jeremiah Blocki, Avrim Blum, Anupam Datta, and Or Sheffet · 2013
Cited alongside, same era.
Truthful mechanisms for agents that value privacy
Yiling Chen, Stephen Chong, Ian A. Kash, Tal Moran, and Salil Vadhan · 2013
Cited alongside, same era.
Recursive mechanism: towards node differential privacy and unrestricted joins
Shixi Chen and Shuigeng Zhou · 2013
Cited alongside, same era.
Analyzing graphs with node differential privacy
Shiva Prasad Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2013
Conservative or liberal? Personalized differential privacy
Zach Jorgensen, Ting Yu, and Graham Cormode · 2015
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A market framework for eliciting private data
Bo Waggoner, Rafael Frongillo, and Jacob Abernethy · 2015
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Typicality-based stability and privacy
Raef Bassily and Yoav Freund · 2016
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Adaptive learning with robust generalization guarantees
Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, and Zhiwei Steven Wu · 2016
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The possibilities and limitations of private prediction markets
Rachel Cummings, David M. Pennock, and Jennifer Wortman Vaughan · 2016
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Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
Buying private data without verification
Arpita Ghosh, Katrina Ligett, Aaron Roth, and Grant Schoenebeck · 2014
Cited alongside, same era.
Truthful linear regression
Rachel Cummings, Stratis Ioannidis, and Katrina Ligett · 2015
Cited alongside, same era.
Accuracy for sale: Aggregating data with a variance constraint
Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, and Juba Ziani · 2015
Cited alongside, same era.
Differential privacy: Now it’s getting personal
Hamid Ebadi, David Sands, and Gerardo Schneider · 2015
Cited alongside, same era.
Privately learning high-dimensional distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman
Cited in the paper.
Efficient lipschitz extensions for high-dimensional graph statistics and node private degree distributions
Sofya Raskhodnikova and Adam D. Smith · 2016
Later among the works it cites.
Heterogeneous differential privacy
Mohammad Alaggan, Sebastien Gambs, and Anne-Marie Kermarrec · 2017
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BLENDER: Enabling local search with a hybrid differential privacy model
Brendan Avent, Aleksandra Korolova, David Zeber, Torgeir Hovden, and Benjamin Livshits · 2017
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Partitioning-based mechanisms under personalized differential privacy
Haoran Li, Li Xiong, Zhanglong Ji, and Xiaoqian Jiang · 2017
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Tight lower bounds for locally differentially private selection
Jonathan Ullman · 2018
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