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A streaming algorithm is said to be adversarially robust if its accuracy guarantees are maintained even when the data stream is chosen maliciously, by an adaptive adversary.
Probabilistic counting algorithms for data base applications
P. Flajolet and G. N. Martin · 1985
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The space complexity of approximating the frequency moments
N. Alon, Y. Matias, and M. Szegedy · 1999
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Counting distinct elements in a data stream
Z. Bar-Yossef, T. S. Jayram, R. Kumar, D. Sivakumar, and L. Trevisan · 2002
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Finding frequent items in data streams
M. Charikar, K. Chen, and M. Farach-Colton · 2002
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Maintaining stream statistics over sliding windows
M. Datar, A. Gionis, P. Indyk, and R. Motwani · 2002
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Approximate frequency counts over data streams
G. S. Manku and R. Motwani · 2002
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An improved data stream summary: the count-min sketch and its applications
G. Cormode and S. Muthukrishnan · 2005
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What’s hot and what’s not: Tracking most frequent items dynamically
G. Cormode and S. Muthukrishnan · 2005
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Optimal approximations of the frequency moments of data streams
P. Indyk and D. Woodruff · 2005
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Data streams: Algorithms and applications
S. Muthukrishnan · 2005
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Efficient algorithms for constructing (1+epsilon, beta)-spanners in the distributed and streaming models
M. Elkin and J. Zhang · 2006
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Mechanism design via differential privacy
F. McSherry and K. Talwar · 2007
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On the complexity of differentially private data release: efficient algorithms and hardness results
C. Dwork, M. Naor, O. Reingold, G. N. Rothblum, and S. P. Vadhan · 2009
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Boosting and differential privacy
C. Dwork, G. N. Rothblum, and S. P. Vadhan · 2010
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A multiplicative weights mechanism for privacy-preserving data analysis
M. Hardt and G. N. Rothblum · 2010
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On the exact space complexity of sketching and streaming small norms
D. M. Kane, J. Nelson, and D. P. Woodruff · 2010
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An optimal algorithm for the distinct elements problem
D. M. Kane, J. Nelson, and D. P. Woodruff · 2010
Cited alongside, same era.
Sketching in adversarial environments
I. Mironov, M. Naor, and G. Segev · 2011
Cited alongside, same era.
Sketching and streaming algorithms
J. Nelson · 2011
Cited alongside, same era.
Preserving statistical validity in adaptive data analysis
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and A. L. Roth · 2015
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Interactive fingerprinting codes and the hardness of preventing false discovery
T. Steinke and J. Ullman · 2015
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Algorithmic stability for adaptive data analysis
R. Bassily, K. Nissim, A. D. Smith, T. Steinke, U. Stemmer, and J. Ullman · 2016
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Continuous monitoring of l_p norms in data streams
J. Blasiok, J. Ding, and J. Nelson · 2017
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Accessing data while preserving privacy
G. Kellaris, G. Kollios, K. Nissim, and A. O’Neill · 2017
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Tighter bounds on multi-party coin flipping via augmented weak martingales and differentially private sampling
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K. J. Ahn, S. Guha, and A. McGregor · 2012
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Graph sketches: sparsification, spanners, and subgraphs
K. J. Ahn, S. Guha, and A. McGregor · 2012
Cited alongside, same era.
Recovering simple signals
A. C. Gilbert, B. Hemenway, A. Rudra, M. J. Strauss, and M. Wootters · 2012
Cited alongside, same era.
Reusable low-error compressive sampling schemes through privacy
A. C. Gilbert, B. Hemenway, M. J. Strauss, D. P. Woodruff, and M. Wootters · 2012
Cited alongside, same era.
Private learning and sanitization: Pure vs. approximate differential privacy
A. Beimel, K. Nissim, and U. Stemmer · 2013
Cited alongside, same era.
How robust are linear sketches to adaptive inputs?
M. Hardt and D. P. Woodruff · 2013
Cited alongside, same era.
A. Beimel, I. Haitner, N. Makriyannis, and E. Omri · 2018
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Composable and versatile privacy via truncated CDP
M. Bun, C. Dwork, G. N. Rothblum, and T. Steinke · 2018
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Data streams with bounded deletions
R. Jayaram and D. P. Woodruff · 2018
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The limits of post-selection generalization
K. Nissim, A. D. Smith, T. Steinke, U. Stemmer, and J. Ullman · 2018
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The adversarial robustness of sampling
O. Ben-Eliezer and E. Yogev · 2019
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Privately learning thresholds: Closing the exponential gap
H. Kaplan, K. Ligett, Y. Mansour, M. Naor, and U. Stemmer · 2019
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Concentration bounds for high sensitivity functions through differential privacy
K. Nissim and U. Stemmer · 2019
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A framework for adversarially robust streaming algorithms
O. Ben-Eliezer, R. Jayaram, D. P. Woodruff, and E. Yogev · 2020
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