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The streaming model of computation is a popular approach for working with large-scale data.
Counting large numbers of events in small registers
Robert Morris · 1978
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Counting large numbers of events in small registers
Robert H. Morris Sr · 1978
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Finding repeated elements
Jayadev Misra and David Gries · 1982
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Probabilistic counting algorithms for data base applications
Philippe Flajolet and G Nigel Martin · 1985
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Randomness-efficient oblivious sampling
Mihir Bellare and John Rompel · 1994
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The space complexity of approximating the frequency moments
Noga Alon, Yossi Matias, and Mario Szegedy · 1996
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Finding frequent items in data streams
Moses Charikar, Kevin Chen, and Martin Farach-Colton · 2002
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Maintaining stream statistics over sliding windows
Mayur Datar, Aristides Gionis, Piotr Indyk, and Rajeev Motwani · 2002
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Space lower bounds for distance approximation in the data stream model
Michael Saks and Xiaodong Sun · 2002
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Loglog counting of large cardinalities
Marianne Durand and Philippe Flajolet · 2003
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An information statistics approach to data stream and communication complexity
Ziv Bar-Yossef, Thathachar S Jayram, Ravi Kumar, and D Sivakumar · 2004
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Tabulation based 4-universal hashing with applications to second moment estimation
Mikkel Thorup and Yin Zhang · 2004
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An improved data stream summary: the count-min sketch and its applications
Graham Cormode and Shan Muthukrishnan · 2005
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Optimal approximations of the frequency moments of data streams
Piotr Indyk and David Woodruff · 2005
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Stable distributions, pseudorandom generators, embeddings, and data stream computation
Piotr Indyk · 2006
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Smooth histograms for sliding windows
Vladimir Braverman and Rafail Ostrovsky · 2007
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Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm
Philippe Flajolet, Éric Fusy, Olivier Gandouet, and Frédéric Meunier · 2007
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Estimators and tail bounds for dimension reduction in l α \alpha (0< α ≤ \alpha\leq 2) using stable random projections
Ping Li · 2008
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Private and continual release of statistics
T-H. Hubert Chan, Elaine Shi, and Dawn Song · 2010
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On the exact space complexity of sketching and streaming small norms
Daniel M Kane, Jelani Nelson, and David P Woodruff · 2010
Cited alongside, same era.
Streaming algorithms via precision sampling
Alexandr Andoni, Robert Krauthgamer, and Krzysztof Onak · 2011
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Pure differential privacy for rectangle queries via private partitions
Cynthia Dwork, Moni Naor, Omer Reingold, and Guy N. Rothblum · 2015
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The price of differential privacy for online learning
Naman Agarwal and Karan Singh · 2017
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High frequency moments via max-stability
Alexandr Andoni · 2017
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Differentially private ordinary least squares
Or Sheffet · 2017
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Differentially private continual release of graph statistics, 2018
Shuang Song, Susan Little, Sanjay Mehta, Staal Vinterbo, and Kamalika Chaudhuri · 2018
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Polynomial estimators for high frequency moments
Sumit Ganguly · 2011
Cited alongside, same era.
Tight bounds for lp samplers, finding duplicates in streams, and related problems
Hossein Jowhari, Mert Sağlam, and Gábor Tardos · 2011
Cited alongside, same era.
Fast moment estimation in data streams in optimal space
Daniel M Kane, Jelani Nelson, Ely Porat, and David P Woodruff · 2011
Cited alongside, same era.
Pan-private algorithms via statistics on sketches
Darakhshan Mir, Shan Muthukrishnan, Aleksandar Nikolov, and Rebecca N Wright · 2011
Cited alongside, same era.
The johnson-lindenstrauss transform itself preserves differential privacy
Jeremiah Blocki, Avrim Blum, Anupam Datta, and Or Sheffet · 2012
Cited alongside, same era.
Differentially private continual monitoring of heavy hitters from distributed streams
T-H Hubert Chan, Mingfei Li, Elaine Shi, and Wenchang Xu · 2012
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Private continual release of real-valued data streams
Victor Perrier, Hassan Jameel Asghar, and Dali Kaafar · 2019
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Sublinear space private algorithms under the sliding window model
Jalaj Upadhyay · 2019
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Differentially-private multi-party sketching for large-scale statistics
Seung Geol Choi, Dana Dachman-Soled, Mukul Kulkarni, and Arkady Yerukhimovich · 2020
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The flajolet-martin sketch itself preserves differential privacy: Private counting with minimal space
Adam D. Smith, Shuang Song, and Abhradeep Thakurta · 2020
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Fast and memory efficient differentially private-sgd via JL projections
Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, and Uthaipon Tantipongpipat · 2021
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Differentially private algorithms for graphs under continual observation
Hendrik Fichtenberger, Monika Henzinger, and Wolfgang Ost · 2021
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A survey on statistical, information, and estimation—theoretic views on privacy
Hsiang Hsu, Natalia Martinez, Martin Bertran, Guillermo Sapiro, and Flavio P. Calmon · 2021
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The price of differential privacy under continual observation
Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar, and Adam D. Smith · 2021
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Jeremiah Blocki, Elena Grigorescu, Tamalika Mukherjee, and Samson Zhou · 2022
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Differentially private fractional frequency moments estimation with polylogarithmic space
Lun Wang, Iosif Pinelis, and Dawn Song · 2022
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