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We study the problem faced by a data analyst or platform that wishes to collect private data from privacy-aware agents.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Incentive compatible regression learning
Ofer Dekel, Felix Fischer, and Ariel D Procaccia · 2010
Earlier work this paper cites.
Strategyproof classification
Reshef Meir and Jeffrey S Rosenschein · 2011
Earlier work this paper cites.
Approximately optimal auctions for selling privacy when costs are correlated with data
Lisa K Fleischer and Yu-Han Lyu · 2012
Earlier work this paper cites.
Algorithms for strategyproof classification
Reshef Meir, Ariel D Procaccia, and Jeffrey S Rosenschein · 2012
Earlier work this paper cites.
Privacy-aware mechanism design
Kobbi Nissim, Claudio Orlandi, and Rann Smorodinsky · 2012
Earlier work this paper cites.
Conducting truthful surveys, cheaply
Aaron Roth and Grant Schoenebeck · 2012
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Privacy and coordination: Computing on databases with endogenous participation
Arpita Ghosh and Katrina Ligett · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Buying private data without verification
Arpita Ghosh, Katrina Ligett, Aaron Roth, and Grant Schoenebeck · 2014
Earlier work this paper cites.
Low-cost learning via active data procurement
Jacob Abernethy, Yiling Chen, Chien-Ju Ho, and Bo Waggoner · 2015
Cited alongside, same era.
Optimum statistical estimation with strategic data sources
Yang Cai, Constantinos Daskalakis, and Christos Papadimitriou · 2015
Cited alongside, same era.
Accuracy for sale: Aggregating data with a variance constraint
Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, and Juba Ziani · 2015
Cited alongside, same era.
Selling privacy at auction
Arpita Ghosh and Aaron Roth · 2015
Cited alongside, same era.
Pricing private data
Vasilis Gkatzelis, Christina Aperjis, and Bernardo A Huberman · 2015
Cited alongside, same era.
Learning to incentivize: Eliciting effort via output agreement
Yang Liu and Yiling Chen · 2016
Cited alongside, same era.
A marketplace for data: An algorithmic solution
Anish Agarwal, Munther Dahleh, and Tuhin Sarkar · 2019
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Markets for information: An introduction
Dirk Bergemann and Alessandro Bonatti · 2019
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Prior-free data acquisition for accurate statistical estimation
Yiling Chen and Shuran Zheng · 2019
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Learning strategy-aware linear classifiers
Yiling Chen, Yang Liu, and Chara Podimata · 2020
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Social-aware privacy-preserving mechanism for correlated data
Guocheng Liao, Xu Chen, and Jianwei Huang · 2020
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Surrogate scoring rules
Yang Liu, Juntao Wang, and Yiling Chen · 2020
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Sequential peer prediction: Learning to elicit effort using posted prices
Yang Liu and Yiling Chen · 2017
Cited alongside, same era.
Optimal data acquisition for statistical estimation
Yiling Chen, Nicole Immorlica, Brendan Lucier, Vasilis Syrgkanis, and Juba Ziani · 2018
Cited alongside, same era.
Too much data: Prices and inefficiencies in data markets
Daron Acemoglu, Ali Makhdoumi, Azarakhsh Malekian, and Asuman Ozdaglar · 2019
Cited alongside, same era.
Truthful linear regression
Rachel Cummings, Stratis Ioannidis, and Katrina Ligett
Cited in the paper.
Optimal data acquisition with privacy-aware agents
Rachel Cummings, Hadi Elzayn, Vasilis Gkatzelis, Emmanouil Pountorakis, and Juba Ziani
Cited in the paper.
The impossibility of strategy-proof clustering
Juan Perote and J Perote-Pena
Cited in the paper.
Mean estimation with user-level privacy under data heterogeneity
Rachel Cummings, Vitaly Feldman, Audra McMillan, and Kunal Talwar · 2021
Later among the works it cites.
Optimal and differentially private data acquisition: Central and local mechanisms
Alireza Fallah, Ali Makhdoumi, Azarakhsh Malekian, and Asuman Ozdaglar · 2022
Closest in time.
The privacy paradox and optimal bias-variance trade-offs in data acquisition
Guocheng Liao, Yu Su, Juba Ziani, Adam Wierman, and Jianwei Huang · 2022
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