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Agencies, such as the U.S.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2013
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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Allocating grants for title i
W. Sonnenberg · 2016
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Fairness in machine learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2017
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Exposed! a survey of attacks on private data
Cynthia Dwork, Adam Smith, Thomas Steinke, and Jonathan Ullman · 2017
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Learning with privacy at scale
Apple Differential Privacy Team · 2017
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The complexity of differential privacy
Salil Vadhan · 2017
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The us census bureau adopts differential privacy
John M Abowd · 2018
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Privacy for all: Ensuring fair and equitable privacy protections
Michael D Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan · 2018
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Differentially private fair learning
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Towards practical differential privacy for sql queries
Noah Johnson, Joseph P Near, and Dawn Song · 2018
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An economic analysis of privacy protection and statistical accuracy as social choices
John M Abowd and Ian M Schmutte · 2019
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On the compatibility of privacy and fairness
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 · 2020
Later among the works it cites.
Bias and variance of post-processing in differential privacy, 2020
Keyu Zhu, Pascal Van Hentenryck, and Ferdinando Fioretto · 2020
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Title 13, u.s. code
Title 13 · 2021
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Differential privacy of hierarchical census data: An optimization approach
Ferdinando Fioretto, Pascal Van Hentenryck, and Keyu Zhu · 2021
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What is gdpr, the eu’s new data protection law?
GDPR · 2021
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Pro rata vaccine distribution is fair, equitable
Lisa Simunaci · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
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Fair decision making using privacy-protected data, 2020
Satya Kuppam, Ryan Mckenna, David Pujol, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau · 2020
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Piecewise linear function fitting via mixed-integer linear programming
Steffen Rebennack and Vitaliy Krasko · 2020
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Differentially private and fair deep learning: A lagrangian dual approach
Cuong Tran, Ferdinando Fioretto, and Pascal Van Hentenryck · 2021
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Decision making with differential privacy under the fairness lens
Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck, and Zhiyan Yao · 2021
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Bias and variance of post-processing in differential privacy
Keyu Zhu, Pascal Van Hentenryck, and Ferdinando Fioretto · 2021
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