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Differential privacy has become the standard for private data analysis, and an extensive literature now offers differentially private solutions to a wide variety of problems.
MapReduce: Simplified Data Processing on Large Clusters
Jeffrey Dean and Sanjay Ghemawat · 2004
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Mechanism Design via Differential Privacy
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
Releasing Search Queries and Clicks Privately
Aleksandra Korolova, Krishnaram Kenthapadi, Nina Mishra, and Alexandros Ntoulas · 2009
Earlier work this paper cites.
Privacy Integrated Queries
Frank McSherry · 2009
Earlier work this paper cites.
A Model of Computation for MapReduce
Howard Karloff, Siddharth Suri, and Sergei Vassilvitskii · 2010
Earlier work this paper cites.
Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
Earlier work this paper cites.
Connected Components in MapReduce and Beyond
Raimondas Kiveris, Silvio Lattanzi, Vahab Mirrokni, Vibhor Rastogi, and Sergei Vassilvitskii · 2014
Earlier work this paper cites.
The Composition Theorem for Differential Privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
Earlier work this paper cites.
Collecting Telemetry Data Privately
Bolin Ding, Janardhan Kulkarni, and Sergei Yekhanin · 2017
Cited alongside, same era.
Webis-tldr-17 corpus
Shahbaz Syed, Michael Voelske, Martin Potthast, and Benno Stein · 2017
Cited alongside, same era.
Learning with Privacy at Scale
Apple Differential Privacy Team · 2017
Cited alongside, same era.
Towards Practical Differential Privacy for SQL Queries
Noah Johnson, Joseph P. Near, and Dawn Song · 2018
Cited alongside, same era.
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy
Kareem Amin, Alex Kulesza, Andrés Muñoz Medina, and Sergei Vassilvtiskii · 2019
Cited alongside, same era.
Smoothly Bounding User Contributions in Differential Privacy
Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, and Lijie Ren · 2020
Later among the works it cites.
Differentially Private Set Union
Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, and Sergey Yekhanin · 2020
Later among the works it cites.
Differentially Private SQL with Bounded User Contribution
Royce J Wilson, Celia Yuxin Zhang, William Lam, Damien Desfontaines, Daniel Simmons-Marengo, and Bryant Gipson · 2020
Later among the works it cites.
LinkedIn’s Audience Engagements API: A Privacy Preserving Data Analytics System at Scale
Ryan Rogers, Subbu Subramaniam, Sean Peng, David Durfee, Seunghyun Lee, Santosh Kumar Kancha, Shraddha Sahay, and Parvez Ahammad · 2021
Later among the works it cites.
Opacus: User-Friendly Differential Privacy Library in PyTorch
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Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa, and Killian Levacher · 2019
Cited alongside, same era.
Parallel Graph Algorithms in Constant Adaptive Rounds: Theory Meets Practice
Soheil Behnezhad, Laxman Dhulipala, Hossein Esfandiari, Jakub Lacki, Vahab Mirrokni, and Warren Schudy · 2020
Cited alongside, same era.
Differentially Private Partition Selection
Damien Desfontaines, James Voss, Bryant Gipson, and Chinmoy Mandayam · 2020
Cited alongside, same era.
Optimal Differential Privacy Composition for Exponential Mechanisms and the Cost of Adaptivity
Jinshuo Dong, David Durfee, and Ryan Rogers · 2020
Cited alongside, same era.
A. Yousefpour, I. Shilov, A. Sablayrolles, D. Testuggine, K. Prasad, M. Malek, J. Nguyen, S. Ghosh, A. Bharadwaj, J. Zhao, G. Cormode, and I. Mironov · 2021
Later among the works it cites.
Google’s collection of differential privacy libraries
Google · 2022
Closest in time.
Google’s privacy on beam library
Google · 2022
Closest in time.
Hyperparameter Tuning with Renyi Differential Privacy
Nicolas Papernot and Thomas Steinke · 2022
Closest in time.
SmartNoise Differential Privacy Library
SmartNoise · 2022
Closest in time.