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The exponential increase in the amount of available data makes taking advantage of them without violating users' privacy one of the fundamental problems of computer science.
Finding repeated elements
Jayadev Misra and David Gries · 1982
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The space complexity of approximating the frequency moments
Noga Alon, Yossi Matias, and Mario Szegedy · 1996
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Loglog counting of large cardinalities
Marianne Durand and Philippe Flajolet · 2003
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Differential Privacy
Cynthia Dwork · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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An improved constant-time approximation algorithm for maximum˜ matchings
Yuichi Yoshida, Masaki Yamamoto, and Hiro Ito · 2009
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Pan-private algorithms via statistics on sketches
Darakhshan Mir, Shan Muthukrishnan, Aleksandar Nikolov, and Rebecca N Wright · 2011
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The johnson-lindenstrauss transform itself preserves differential privacy
Jeremiah Blocki, Avrim Blum, Anupam Datta, and Or Sheffet · 2012
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Estimating the number of connected components in sublinear time
Petra Berenbrink, Bruce Krayenhoff, and Frederik Mallmann-Trenn · 2014
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Quantitative risk management: concepts, techniques and tools-revised edition
Alexander J McNeil, Rüdiger Frey, and Paul Embrechts · 2015
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Efficient private statistics with succinct sketches
Luca Melis, George Danezis, and Emiliano De Cristofaro · 2015
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A simpler sublinear algorithm for approximating the triangle count
C Seshadhri · 2015
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Optimal quantile approximation in streams
Zohar Karnin, Kevin Lang, and Edo Liberty · 2016
Cited alongside, same era.
Privacy odometers and filters: Pay-as-you-go composition
Ryan M Rogers, Aaron Roth, Jonathan Ullman, and Salil Vadhan · 2016
Cited alongside, same era.
Practical locally private heavy hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, and Abhradeep Guha Thakurta · 2017
Cited alongside, same era.
On approximating the number of k-cliques in sublinear time
Talya Eden, Dana Ron, and C Seshadhri · 2018
Cited alongside, same era.
High-dimensional probability: An introduction with applications in data science , volume 47
Roman Vershynin · 2018
Cited alongside, same era.
Sublinear time algorithms: Connected components, average degree
Sepehr Assadi · 2020
Cited alongside, same era.
Countsketches, feature hashing and the median of three
Kasper Green Larsen, Rasmus Pagh, and Jakub Tětek · 2021
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Improved differentially private euclidean distance approximation
Nina Mesing Stausholm · 2021
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Differentially private fractional frequency moments estimation with polylogarithmic space
Lun Wang, Iosif Pinelis, and Dawn Song · 2021
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Bounded space differentially private quantiles
Daniel Alabi, Omri Ben-Eliezer, and Anamay Chaturvedi · 2022
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Privately estimating graph parameters in sublinear time
J Blocki, E Grigorescu, and T Mukherjee · 2022
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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
Cited alongside, same era.
Differentially private sublinear average degree approximation, 2020
Harry Sivasubramaniam, Haonan Li, and Xi He · 2020
Cited alongside, same era.
The flajolet-martin sketch itself preserves differential privacy: Private counting with minimal space
Adam Smith, Shuang Song, and Abhradeep Guha Thakurta · 2020
Cited alongside, same era.
On the power of multiple anonymous messages: Frequency estimation and selection in the shuffle model of differential privacy
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, and Ameya Velingker · 2021
Cited alongside, same era.
Frequency estimation under multiparty differential privacy: One-shot and streaming
Ziyue Huang, Yuan Qiu, Ke Yi, and Graham Cormode · 2021
Cited alongside, same era.
A note on sanitizing streams with differential privacy
Haim Kaplan and Uri Stemmer · 2021
Cited alongside, same era.
Jonas Boehler and Florian Kerschbaum · 2022
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(nearly) all cardinality estimators are differentially private
Charlie Dickens, Justin Thaler, and Daniel Ting · 2022
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Does f X + Y ( z ) = e [ f y ( z − x ) ] f_{X+Y}(z)=e[f_{y}(z-x)] hold?
geetha290krm (https://math.stackexchange.com/users/1064504/geetha290krm) · 2022
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Improved utility analysis of private countsketch
Rasmus Pagh and Mikkel Thorup · 2022
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Differentially private linear sketches: Efficient implementations and applications
Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, and Yu-Xiang Wang · 2022
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Differentially private continual releases of streaming frequency moment estimations
Alessandro Epasto, Jieming Mao, Andres Munoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, and Peilin Zhong · 2023
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Better differentially private approximate histograms and heavy hitters using the misra-gries sketch
Christian Janos Lebeda and Jakub Tětek · 2023
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