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We consider a platform's problem of collecting data from privacy sensitive users to estimate an underlying parameter of interest.
Optimal auction design
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K. Ligett and A. Roth · 2012
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Redrawing the boundaries on purchasing data from privacy-sensitive individuals
K. Nissim, S. Vadhan, and D. Xiao · 2014
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Heterogeneous differential privacy
M. Alaggan, S. Gambs, and A.-M. Kermarrec · 2015
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Selling cookies
D. Bergemann and A. Bonatti · 2015
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Optimum statistical estimation with strategic data sources
Y. Cai, C. Daskalakis, and C. Papadimitriou · 2015
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Accuracy for sale: Aggregating data with a variance constraint
R. Cummings, K. Ligett, A. Roth, Z. S. Wu, and J. Ziani · 2015
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Conservative or liberal? personalized differential privacy
Z. Jorgensen, T. Yu, and G. Cormode · 2015
Clearing matching markets efficiently: informative signals and match recommendations
I. Ashlagi, M. Braverman, Y. Kanoria, and P. Shi · 2020
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Optimal signaling of content accuracy: Engagement vs. misinformation
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Providing data samples for free
K. Drakopoulos and A. Makhdoumi · 2020
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Privacy management in service systems
M. Hu, R. Momot, and J. Wang · 2020
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When does competition and costly information acquisition lead to a deadlock?
N. Immorlica, Y. Kanoria, and J. Lu · 2020
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Inferring consideration sets from sales transaction data
S. Jagabathula, D. Mitrofanov, and G. Vulcano · 2020
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Information sharing in a supply chain with a common retailer
W. Shang, A. Y. Ha, and S. Tong · 2015
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Learning in games: Robustness of fast convergence
D. J. Foster, Z. Li, T. Lykouris, K. Sridharan, and E. Tardos · 2016
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Selling information
J. Hörner and A. Skrzypacz · 2016
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Learning to incentivize: Eliciting effort via output agreement
Y. Liu and Y. Chen · 2016
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Collecting telemetry data privately
B. Ding, J. Kulkarni, and S. Yekhanin · 2017
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Finite sample differentially private confidence intervals
V. Karwa and S. Vadhan · 2017
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Private mean estimation of heavy-tailed distributions
G. Kamath, V. Singhal, and J. Ullman · 2020
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Privacy-preserving personalized revenue management
Y. M. Lei, S. Miao, and R. Momot · 2020
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Too much data: Prices and inefficiencies in data markets
D. Acemoglu, A. Makhdoumi, A. Malekian, and A. Ozdaglar · 2021
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Differentially private assouad, fano, and le cam
J. Acharya, Z. Sun, and H. Zhang · 2021
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Information design for congested social services: Optimal need-based persuasion
J. Anunrojwong, K. Iyer, and V. Manshadi · 2021
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Optimal dynamic allocation: Simplicity through information design
I. Ashlagi, F. Monachou, and A. Nikzad · 2021
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Local differential privacy is equivalent to contraction of an f-divergence
S. Asoodeh, M. Aliakbarpour, and F. P. Calmon · 2021
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How big should your data really be? data-driven newsvendor and the transient of learning
O. Besbes and O. Mouchtaki · 2021
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Data tracking under competition
K. Bimpikis, I. Morgenstern, and D. Saban · 2021
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Mean estimation with user-level privacy under data heterogeneity
R. Cummings, V. Feldman, A. McMillan, and K. Talwar · 2021
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Generalized linear bandits with local differential privacy
Y. Han, Z. Liang, Y. Wang, and J. Zhang · 2021
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The economics of data externalities
S. Ichihashi · 2021
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Buying data over time: Approximately optimal strategies for dynamic data-driven decisions
N. Immorlica, I. A. Kash, and B. Lucier · 2021
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The privacy paradox and optimal bias-variance trade-offs in data acquisition
G. Liao, Y. Su, J. Ziani, A. Wierman, and J. Huang · 2021
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Towards explaining epsilon: A worst-case study of differential privacy risks
L. Mehner, S. N. von Voigt, and F. Tschorsch · 2021
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Adapdp: Adaptive personalized differential privacy
B. Niu, Y. Chen, B. Wang, Z. Wang, F. Li, and J. Cao · 2021
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Mechanism design under approximate incentive compatibility
S. R. Balseiro, O. Besbes, and F. Castro · 2022
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Optimal data acquisition with privacy-aware agents
R. Cummings, H. Elzayn, V. Gkatzelis, E. Pountourakis, and J. Ziani · 2022
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Differential privacy overview - apple
Apple · 2023
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