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We generalize the continuous observation privacy setting from Dwork et al.
Practical differentially private top-k selection with pay-what-you-get composition
D. Durfee and R. Rogers · 1905
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Our data, ourselves: Privacy via distributed noise generation
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor · 2006
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Mechanism design via differential privacy
F. McSherry and K. Talwar · 2007
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On the complexity of differentially private data release: Efficient algorithms and hardness results
C. Dwork, M. Naor, O. Reingold, G. N. Rothblum, and S. Vadhan · 2009
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Releasing search queries and clicks privately
A. Korolova, K. Kenthapadi, N. Mishra, and A. Ntoulas · 2009
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Pan-private streaming algorithms
C. Dwork, M. Naor, T. Pitassi, G. N. Rothblum, and S. Yekhanin · 2010
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Private and continual release of statistics
T. H. Chan, E. Shi, and D. Song · 2011
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Pan-private algorithms via statistics on sketches
D. Mir, S. Muthukrishnan, A. Nikolov, and R. N. Wright · 2011
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Differentially private continual monitoring of heavy hitters from distributed streams
T.-H. H. Chan, M. Li, E. Shi, and W. Xu · 2012
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(nearly) optimal algorithms for private online learning in full-information and bandit settings
A. Guha Thakurta and A. Smith · 2013
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Understanding hierarchical methods for differentially private histograms
W. Qardaji, W. Yang, and N. Li · 2013
Cited alongside, same era.
The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
Cited alongside, same era.
Private matchings and allocations
J. Hsu, Z. Huang, A. Roth, T. Roughgarden, and Z. S. Wu · 2014
Cited alongside, same era.
Asymptotically truthful equilibrium selection in large congestion games
R. M. Rogers and A. Roth · 2014
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
M. Bun and T. Steinke · 2016
Cited alongside, same era.
The complexity of differential privacy
S. Vadhan · 2017
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Differentially private online submodular minimization
A. R. Cardoso and R. Cummings · 2019
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The discrete gaussian for differential privacy, 2020
C. L. Canonne, G. Kamath, and T. Steinke · 2020
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Bounding, concentrating, and truncating: Unifying privacy loss composition for data analytics, 2020
M. Cesar and R. Rogers · 2020
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Guidelines for implementing and auditing differentially private systems
D. Kifer, S. Messing, A. Roth, A. Thakurta, and D. Zhang · 2020
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Linkedin’s audience engagements api: A privacy preserving data analytics system at scale, 2020
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Simultaneous private learning of multiple concepts
M. Bun, K. Nissim, and U. Stemmer · 2016
Cited alongside, same era.
Understanding the sparse vector technique for differential privacy
M. Lyu, D. Su, and N. Li · 2017
Cited alongside, same era.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith
Cited in the paper.
Differential privacy under continual observation
C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum
Cited in the paper.
R. Rogers, S. Subramaniam, S. Peng, D. Durfee, S. Lee, S. K. Kancha, S. Sahay, and P. Ahammad · 2020
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Differentially private sql with bounded user contribution
R. Wilson, C. Y. Zhang, W. Lam, D. Desfontaines, D. Simmons-Marengo, and B. Gipson · 2020
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The price of differential privacy under continual observation, 2021
P. Jain, S. Raskhodnikova, S. Sivakumar, and A. Smith · 2021
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