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An exciting new development in differential privacy is the shuffled model, in which an anonymous channel enables non-interactive, differentially private protocols with error much smaller than what is possible in the local model, while relying on weaker trust assumptions than in the central model.
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Graham Cormode and Shan Muthukrishnan · 2005
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Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Cynthia Dwork · 2006
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Binomial approximation to the Poisson binomial distribution: The Krawtchouk Expansion
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Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian · 2007
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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A learning theory approach to non-interactive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2008
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Finding frequent items in data streams
Graham Cormode and Marios Hadjieleftheriou · 2008
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Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2008
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Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Rashkodnikova, and Adam Smith · 2008
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Joe Kilian, André Madeira, Martin J Strauss, and Xuan Zheng · 2008
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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Cynthia Dwork, Moni Naor, Omer Reingold, Guy N Rothblum, and Salil Vadhan · 2009
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Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N. Rothblum · 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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Boosting the accuracy of differentially private histograms through consistency
Michael Hay, Vibhor Rastogi, Gerome Miklau, and Dan Suciu · 2010
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Optimizing linear counting queries under differential privacy
Chao Li, Michael Hay, Vibhor Rastogi, Gerome Milau, and Andrew McGregor · 2010
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A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
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Differential privacy via wavelet transforms
Xiaokui Xiao, Guozhang Wang, and Johannes Gehrke · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
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Sketch techniques for approximate query processing
Graham Cormode · 2011
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Private and continual release of statistics
T.-H. Hubert Chan, Elaine Shi, and Dawn Song · 2011
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Differentially private m m -estimators
Jing Lei · 2011
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Privacy-preserving statistical estimation with optimal convergence rates
Adam D. Smith · 2011
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Concentration Inequalities: a nonasmpytotic theory of independence
Stephane Boucheron, Gabor Lugosi, and Pascal Massart · 2012
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Learning with privacy at scale
Apple Differential Privacy Team · 2017
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Prochlo: Strong privacy for analytics in the crowd
Andrea Bittau, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnés, and Bernhard Seefeld · 2017
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Practical locally private heavy hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, and Abhradeep Guha Thakurta · 2017
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Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
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Tight lower bounds for differentially private selection
Thomas Steinke and Jonathan Ullman · 2017
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The complexity of differential privacy
Salil Vadhan · 2017
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Differentially private spatial decompositions
Graham Cormode, Cecilia Procopiuc, Divesh Srivastava, Entong Shen, and Ting Yu · 2012
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Optimal lower bound for differentially private multi-part aggregation
T-H. Hubert Chan, Elaine Shi, and Dawn Song · 2012
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Distributed private heavy hitters
Justin Hsu, Sanjeev Khanna, and Aaron Roth · 2012
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A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank McSherry · 2012
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An adaptive mechanism for accurate query answering under differential privacy
Chao Li and Gerome Miklau · 2012
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Optimal private halfspace counting via discrepancy
S. Muthukrishnan and Aleksandar Nikolov · 2012
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Locally differentially private protocols for frequency estimation
Tianhao Wang, Jeremiah Blocki, Ninghui Li, and Somesh Jha · 2017
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Optimal schemes for discrete distribution estimation under local differential privacy
Min Ye and Alexander Barg · 2017
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cpsgd: Communication-efficient and differentially-private distributed sgd
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan · 2018
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Heavy hitters and the structure of local privacy
Mark Bun, Jelani Nelson, and Uri Stemmer · 2018
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Marginal release under local differential privacy
Graham Cormode, Tejas Kulkarni, and Divesh Srivastava · 2018
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Minimax optimal procedures for locally private estimation
John C. Duchi, Michael I. Jordan, and Martin J. Wainwright · 2018
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Tight lower bounds for locally differentially private selection
Jonathan Ullman · 2018
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Test without trust: Optimal locally private distribution testing
Jayadev Acharya, Clément Canonne, Cody Freitag, and Himanshu Tyagi · 2019
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Jayadev Acharya and Ziteng Sun · 2019
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Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2019
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Differentially private summation with multi-message shuffling
Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim · 2019
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Improved summation from shuffling
Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim · 2019
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Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim · 2019
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Separating local & shuffled differential privacy via histograms, 2019
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Albert Cheu, Adam D. Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019
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Amplification by shuffling: From local to central differential privacy via anonymity
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Private aggregation from fewer anonymous messages
Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, and Ameya Velingker · 2019
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Scalable and differentially private distributed aggregation in the shuffled model
Badih Ghazi, Rasmus Pagh, and Ameya Velingker · 2019
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Practical and robust privacy amplification with multi-party differential privacy
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Private summation in the multi-message shuffle model
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Small Summaries for Big Data
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The power of factorization methods in local and central differential privacy
Alexander Edmonds, Aleksander Nikolov, and Jonathan Ullman · 2020
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Pure differentially private summation from anonymous messages
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, and Ameya Velingker · 2020
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Locally private k-means clustering
Uri Stemmer · 2020
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