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The Gaussian mechanism is an essential building block used in multitude of differentially private data analysis algorithms.
Values of mills’ ratio of area to bounding ordinate and of the normal probability integral for large values of the argument
Robert D Gordon · 1941
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Inadmissibility of the usual estimator for the mean of a multivariate normal distribution
Charles Stein · 1956
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Minimax estimation of the mean of a normal distribution when the parameter space is restricted
PJ Bickel et al · 1981
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Minimax risk over hyperrectangles, and implications
David L Donoho, Richard C Liu, and Brenda MacGibbon · 1990
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Minimax risk over p-balls for p-error
David L Donoho and Iain M Johnstone · 1994
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De-noising by soft-thresholding
David L Donoho · 1995
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On power-law relationships of the internet topology
Michalis Faloutsos, Petros Faloutsos, and Christos Faloutsos · 1999
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Gaussian model selection
Lucien Birgé and Pascal Massart · 2001
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SciPy: Open source scientific tools for Python, 2001
Eric Jones, Travis Oliphant, Pearu Peterson, et al · 2001
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Theory of point estimation
Erich L Lehmann and George Casella · 2006
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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, and Kunal Talwar · 2007
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 Vadhan · 2009
Cited alongside, same era.
Accurate estimation of the degree distribution of private networks
Michael Hay, Chao Li, Gerome Miklau, and David Jensen · 2009
Cited alongside, same era.
Probabilistic inference and differential privacy
Oliver Williams and Frank McSherry · 2010
Cited alongside, same era.
The geometry of differential privacy: the sparse and approximate cases
Aleksandar Nikolov, Kunal Talwar, and Li Zhang · 2013
Cited alongside, same era.
Detecting activations over graphs using spanning tree wavelet bases
The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Anonymizing nyc taxi data: Does it matter?
Marie Douriez, Harish Doraiswamy, Juliana Freire, and Cláudio T Silva · 2016
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Concentrated differential privacy
Cynthia Dwork and Guy N Rothblum · 2016
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Inference using noisy degrees: Differentially private β \beta -model and synthetic graphs
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James Sharpnack, Aarti Singh, and Akshay Krishnamurthy · 2013
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Dirichlet draws are sparse with high probability
Matus Telgarsky · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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Using topological analysis to support event-guided exploration in urban data
Harish Doraiswamy, Nivan Ferreira, Theodoros Damoulas, Juliana Freire, and Cláudio T Silva · 2014
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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On taxi and rainbows
Vijay Pandurangan · 2014
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Vishesh Karwa, Aleksandra Slavković, et al · 2016
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Privacy odometers and filters: Pay-as-you-go composition
Ryan M Rogers, Aaron Roth, Jonathan Ullman, and Salil Vadhan · 2016
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Trend filtering on graphs
Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, and Ryan J. Tibshirani · 2016
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Differentially private learning of undirected graphical models using collective graphical models
Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, and Gerome Miklau · 2017
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Understanding the sparse vector technique for differential privacy
Min Lyu, Dong Su, and Ninghui Li · 2017
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Renyi differential privacy
Ilya Mironov · 2017
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