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Modern data aggregation often involves a platform collecting data from a network of users with various privacy options.
A Value for N-Person Games
Lloyd S. Shapley · 1952
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A new interpretation of information rate
John L Kelly · 1956
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Potential, value, and consistency
Sergiu Hart and Andreu Mas-Colell · 1989
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Notes on equilibria in symmetric games
Shih-Fen Cheng, Daniel M Reeves, Yevgeniy Vorobeychik, and Michael P Wellman · 2004
Earlier work this paper cites.
Differential privacy: A survey of results
Cynthia Dwork · 2008
Earlier work this paper cites.
Enumeration of Nash equilibria for two-player games
David Avis, Gabriel D. Rosenberg, Rahul Savani, and Bernhard von Stengel · 2010
Earlier work this paper cites.
Model-sharing games: Analyzing federated learning under voluntary participation
Kate Donahue and Jon Kleinberg · 2010
Earlier work this paper cites.
Data markets in the cloud: An opportunity for the database community
Magdalena Balazinska, Bill Howe, and Dan Suciu · 2011
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Selling privacy at auction
Arpita Ghosh and Aaron Roth · 2011
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Conducting truthful surveys, cheaply
Aaron Roth and Grant Schoenebeck · 2012
Earlier work this paper cites.
Privacy as a coordination game
Arpita Ghosh and Katrina Ligett · 2013
Cited alongside, same era.
The economics of privacy
Alessandro Acquisti, Curtis Taylor, and Liad Wagman · 2016
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 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.
A distributional framework for data valuation
Amirata Ghorbani, Michael Kim, and James Zou · 2020
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Trading data for learning: Incentive mechanism for on-device federated learning
Rui Hu and Yanmin Gong · 2020
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Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 2021
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Ditto: Fair and robust federated learning through personalization
Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith · 2021
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Prior-free data acquisition for accurate statistical estimation
Yiling Chen and Shuran Zheng · 2019
Cited alongside, same era.
Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
Cited alongside, same era.
Towards efficient data valuation based on the shapley value
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, and Costas J. Spanos · 2019
Cited alongside, same era.
Data analytics in a privacy-concerned world
Jaap Wieringa, P.K. Kannan, Xiao Ma, Thomas Reutterer, Hans Risselada, and Bernd Skiera · 2019
Cited alongside, same era.
The challenges of personal data markets and privacy
Sarah Spiekermann, Alessandro Acquisti, Rainer Böhme, and Kai-Lung Hui
Cited in the paper.
Personal data markets
Sarah Spiekermann, Rainer Böhme, Alessandro Acquisti, and Kai-Lung Hui
Cited in the paper.
Alireza Fallah, Ali Makhdoumi, Azarakhsh Malekian, and Asuman Ozdaglar · 2022
Later among the works it cites.
Federated learning with heterogeneous differential privacy, 2023
Nasser Aldaghri, Hessam Mahdavifar, and Ahmad Beirami · 2023
Closest in time.
Mean estimation under heterogeneous privacy: Some privacy can be free, 2023
Syomantak Chaudhuri and Thomas A. Courtade · 2023
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Optimal data acquisition with privacy-aware agents
R. Cummings, H. Elzayn, E. Pountourakis, V. Gkatzelis, and J. Ziani · 2023
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Data banzhaf: A robust data valuation framework for machine learning
Jiachen T. Wang and Ruoxi Jia · 2023
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