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We present novel, computationally efficient, and differentially private algorithms for two fundamental high-dimensional learning problems: learning a multivariate Gaussian and learning a product distribution over the Boolean hypercube in total variation distance.
The accuracy of the Gaussian approximation to the sum of independent variates
Andrew C. Berry · 1941
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On the Liapounoff limit of error in the theory of probability
Carl-Gustaf Esseen · 1942
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Adaptive estimation of a quadratic functional by model selection
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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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 and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil Vadhan · 2010
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On the geometry of differential privacy
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I.G. Shevtsova · 2010
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Privacy-preserving statistical estimation with optimal convergence rates
Adam Smith · 2011
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PCPs and the hardness of generating private synthetic data
Jonathan Ullman and Salil Vadhan · 2011
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GUPT: Privacy preserving data analysis made easy
Prashanth Mohan, Abhradeep Thakurta, Elaine Shi, Dawn Song, and David Culler · 2012
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Topics in random matrix theory
Terence Tao · 2012
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Bounds on the sample complexity for private learning and private data release
Amos Beimel, Hai Brenner, Shiva Prasad Kasiviswanathan, and Kobbi Nissim · 2014
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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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Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2014
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Analyze Gauss: Optimal bounds for privacy-preserving principal component analysis
Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2014
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RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Preventing false discovery in interactive data analysis is hard
Moritz Hardt and Jonathan Ullman · 2014
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Differentially private release and learning of threshold functions
Mark Bun, Kobbi Nissim, Uri Stemmer, and Salil Vadhan · 2015
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Differentially private learning of structured discrete distributions
Ilias Diakonikolas, Moritz Hardt, and Ludwig Schmidt · 2015
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Robust traceability from trace amounts
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 2015
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Interactive fingerprinting codes and the hardness of preventing false discovery
Thomas Steinke and Jonathan Ullman · 2015
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Revisiting differentially private hypothesis tests for categorical data
The modernization of statistical disclosure limitation at the U.S. census bureau, 2017
Aref N. Dajani, Amy D. Lauger, Phyllis E. Singer, Daniel Kifer, Jerome P. Reiter, Ashwin Machanavajjhala, Simson L. Garfinkel, Scot A. Dahl, Matthew Graham, Vishesh Karwa, Hang Kim, Philip Lelerc, Ian M. Schmutte, William N. Sexton, Lars Vilhuber, and John M. Abowd · 2017
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Exposed! a survey of attacks on private data
Cynthia Dwork, Adam Smith, Thomas Steinke, and Jonathan Ullman · 2017
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A new class of private chi-square tests
Daniel Kifer and Ryan M. Rogers · 2017
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Differentially private chi-squared test by unit circle mechanism
Kazuya Kakizaki, Jun Sakuma, and Kazuto Fukuchi · 2017
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Differentially private ordinary least squares
Or Sheffet · 2017
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Membership inference attacks against machine learning models
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Yue Wang, Jaewoo Lee, and Daniel Kifer · 2015
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Robust estimators in high dimensions without the computational intractability
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2016
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Concentrated differential privacy
Cynthia Dwork and Guy N. Rothblum · 2016
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Psi ( ψ \psi ): A private data sharing interface
Marco Gaboardi, James Honaker, Gary King, Jack Murtagh, Kobbi Nissim, Jonathan Ullman, and Salil Vadhan · 2016
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Differentially private chi-squared hypothesis testing: Goodness of fit and independence testing
Marco Gaboardi, Hyun-Woo Lim, Ryan M. Rogers, and Salil P. Vadhan · 2016
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Strong hardness of privacy from weak traitor tracing
Lucas Kowalczyk, Tal Malkin, Jonathan Ullman, and Mark Zhandry · 2016
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Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Between pure and approximate differential privacy
Thomas Steinke and Jonathan Ullman · 2017
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Tight lower bounds for differentially private selection
Thomas Steinke and Jonathan Ullman · 2017
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Differentially private identity and closeness testing of discrete distributions
Maryam Aliakbarpour, Ilias Diakonikolas, and Ronitt Rubinfeld · 2018
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Inspectre: Privately estimating the unseen
Jayadev Acharya, Gautam Kamath, Ziteng Sun, and Huanyu Zhang · 2018
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Differentially private testing of identity and closeness of discrete distributions
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2018
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Robustly learning a Gaussian: Getting optimal error, efficiently
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2018
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The minimax learning rate of normal and Ising undirected graphical models
Luc Devroye, Abbas Mehrabian, and Tommy Reddad · 2018
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Local private hypothesis testing: Chi-square tests
Marco Gaboardi and Ryan Rogers · 2018
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Hardness of non-interactive differential privacy from one-way functions
Lucas Kowalczyk, Tal Malkin, Jonathan Ullman, and Daniel Wichs · 2018
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Finite sample differentially private confidence intervals
Vishesh Karwa and Salil Vadhan · 2018
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Resilience: A criterion for learning in the presence of arbitrary outliers
Jacob Steinhardt, Moses Charikar, and Gregory Valiant · 2018
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Locally private hypothesis testing
Or Sheffet · 2018
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The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy
T. Tony Cai, Yichen Wang, and Linjun Zhang · 2019
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A lower bound for private covariance estimation
Gautam Kamath · 2019
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