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Many data analysis operations can be expressed as a GROUP BY query on an unbounded set of partitions, followed by a per-partition aggregation.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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A discrete analogue of the laplace distribution
Seidu Inusah and Tomasz J Kozubowski · 2006
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Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm
Philippe Flajolet, Éric Fusy, Olivier Gandouet, and Frédéric Meunier · 2007
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On the complexity of differentially private data release: efficient algorithms and hardness results
Cynthia Dwork, Moni Naor, Omer Reingold, Guy N Rothblum, and Salil Vadhan · 2009
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Boosting the accuracy of differentially-private histograms through consistency
Michael Hay, Vibhor Rastogi, Gerome Miklau, and Dan Suciu · 2009
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Releasing search queries and clicks privately
Aleksandra Korolova, Krishnaram Kenthapadi, Nina Mishra, and Alexandros Ntoulas · 2009
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Differential privacy via wavelet transforms
Xiaokui Xiao, Guozhang Wang, and Johannes Gehrke · 2010
Earlier work this paper cites.
Differentially private publication of sparse data
Graham Cormode, Magda Procopiuc, Divesh Srivastava, and Thanh TL Tran · 2011
Earlier work this paper cites.
Differentially private data cubes: optimizing noise sources and consistency
Bolin Ding, Marianne Winslett, Jiawei Han, and Zhenhui Li · 2011
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No free lunch in data privacy
Daniel Kifer and Ashwin Machanavajjhala · 2011
Earlier work this paper cites.
Differentially private histogram publishing through lossy compression
Gergely Acs, Claude Castelluccia, and Rui Chen · 2012
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Differentially private summaries for sparse data
Graham Cormode, Cecilia Procopiuc, Divesh Srivastava, and Thanh TL Tran · 2012
Cited alongside, same era.
Universally utility-maximizing privacy mechanisms
Arpita Ghosh, Tim Roughgarden, and Mukund Sundararajan · 2012
Cited alongside, same era.
Dpcube: differentially private histogram release through multidimensional partitioning
Yonghui Xiao, Li Xiong, Liyue Fan, and Slawomir Goryczka · 2012
Cited alongside, same era.
Differentially private histogram publication
Jia Xu, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, Ge Yu, and Marianne Winslett · 2013
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
Top-k frequent itemsets via differentially private fp-trees
Jaewoo Lee and Christopher W Clifton · 2014
Privbayes: Private data release via bayesian networks
Jun Zhang, Graham Cormode, Cecilia M Procopiuc, Divesh Srivastava, and Xiaokui Xiao · 2017
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Shrinkwrap: Differentially-private query processing in private data federations
Johes Bater, Xi He, William Ehrich, Ashwin Machanavajjhala, and Jennie Rogers · 2018
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Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang · 2018
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Towards practical differential privacy for sql queries
Noah Johnson, Joseph P Near, and Dawn Song · 2018
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Privatesql: a differentially private sql query engine
Ios Kotsogiannis, Yuchao Tao, Xi He, Maryam Fanaeepour, Ashwin Machanavajjhala, Michael Hay, and Gerome Miklau · 2019
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Cited alongside, same era.
Differentially private synthesization of multi-dimensional data using copula functions
Haoran Li, Li Xiong, and Xiaoqian Jiang · 2014
Cited alongside, same era.
Differential privacy in metric spaces: Numerical, categorical and functional data under the one roof
Naoise Holohan, Douglas J Leith, and Oliver Mason · 2015
Cited alongside, same era.
Optimal noise adding mechanisms for approximate differential privacy
Quan Geng and Pramod Viswanath · 2016
Cited alongside, same era.
Understanding the sparse vector technique for differential privacy
Min Lyu, Dong Su, and Ninghui Li · 2016
Cited alongside, same era.
Royce J Wilson, Celia Yuxin Zhang, William Lam, Damien Desfontaines, Daniel Simmons-Marengo, and Bryant Gipson · 2019
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Tight analysis of privacy and utility tradeoff in approximate differential privacy
Quan Geng, Wei Ding, Ruiqi Guo, and Sanjiv Kumar · 2020
Closest in time.
Differentially private set union
Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, and Sergey Yekhanin · 2020
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The sparse vector technique, revisited
Haim Kaplan, Yishay Mansour, and Uri Stemmer · 2020
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Differentially private weighted sampling
Edith Cohen, Ofir Geri, Tamas Sarlos, and Uri Stemmer · 2021
Closest in time.