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Classical streaming algorithms operate under the (not always reasonable) assumption that the input stream is fixed in advance.
The space complexity of approximating the frequency moments
Noga Alon, Yossi Matias, and Mario Szegedy · 1999
Earlier work this paper cites.
Tabulation based 4-universal hashing with applications to second moment estimation
Mikkel Thorup and Yin Zhang · 2004
Earlier work this paper cites.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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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 P. Vadhan · 2009
Earlier work this paper cites.
Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 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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Sketching in adversarial environments
Ilya Mironov, Moni Naor, and Gil Segev · 2011
Earlier work this paper cites.
Analyzing graph structure via linear measurements
Kook Jin Ahn, Sudipto Guha, and Andrew McGregor · 2012
Earlier work this paper cites.
Graph sketches: sparsification, spanners, and subgraphs
Kook Jin Ahn, Sudipto Guha, and Andrew McGregor · 2012
Earlier work this paper cites.
Recovering simple signals
A. C. Gilbert, B. Hemenway, A. Rudra, M. J. Strauss, and M. Wootters · 2012
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Reusable low-error compressive sampling schemes through privacy
A. C. Gilbert, B. Hemenway, M. J. Strauss, D. P. Woodruff, and M. Wootters · 2012
Earlier work this paper cites.
How robust are linear sketches to adaptive inputs?
Moritz Hardt and David P. Woodruff · 2013
Cited alongside, same era.
Preventing false discovery in interactive data analysis is hard
Moritz Hardt and Jonathan R. Ullman · 2014
Cited alongside, same era.
Differentially private release and learning of threshold functions
Mark Bun, Kobbi Nissim, Uri Stemmer, and Salil P. Vadhan · 2015
Cited alongside, same era.
Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
Cited alongside, same era.
Interactive fingerprinting codes and the hardness of preventing false discovery
Thomas Steinke and Jonathan R. Ullman · 2015
Cited alongside, same era.
Private learning and sanitization: Pure vs. approximate differential privacy
Amos Beimel, Kobbi Nissim, and Uri Stemmer · 2016
A new analysis of differential privacy’s generalization guarantees
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld · 2020
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Privately learning thresholds: Closing the exponential gap
Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, and Uri Stemmer · 2020
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Adversarial robustness of streaming algorithms through importance sampling
Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, and Samson Zhou · 2021
Closest in time.
Dynamic algorithms against an adaptive adversary: Generic constructions and lower bounds
Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, and Uri Stemmer · 2021
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, and Jonathan R. Ullman · 2021
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Cited alongside, same era.
Composable and versatile privacy via truncated CDP
Mark Bun, Cynthia Dwork, Guy N. Rothblum, and Thomas Steinke · 2018
Cited alongside, same era.
The limits of post-selection generalization
Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2018
Cited alongside, same era.
The adversarial robustness of sampling
Omri Ben-Eliezer and Eylon Yogev · 2019
Cited alongside, same era.
Concentration bounds for high sensitivity functions through differential privacy
Kobbi Nissim and Uri Stemmer · 2019
Cited alongside, same era.
A necessary and sufficient stability notion for adaptive generalization
Moshe Shenfeld and Katrina Ligett · 2019
Cited alongside, same era.
A framework for adversarially robust streaming algorithms
Omri Ben-Eliezer, Rajesh Jayaram, David P Woodruff, and Eylon Yogev · 2020
Cited alongside, same era.
Closest in time.
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 2021
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Separating adaptive streaming from oblivious streaming using the bounded storage model
Haim Kaplan, Yishay Mansour, Kobbi Nissim, and Uri Stemmer · 2021
Closest in time.
Generalization in the face of adaptivity: A bayesian perspective
Moshe Shenfeld and Katrina Ligett · 2021
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Tight bounds for adversarially robust streams and sliding windows via difference estimators
David P. Woodruff and Samson Zhou · 2021
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On the robustness of countsketch to adaptive inputs
Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, and Uri Stemmer · 2022
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Tricking the hashing trick: A tight lower bound on the robustness of countsketch to adaptive inputs
Edith Cohen, Jelani Nelson, Tamás Sarlós, and Uri Stemmer · 2022
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Adaptive data analysis with correlated observations
Aryeh Kontorovich, Menachem Sadigurschi, and Uri Stemmer · 2022
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