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We revisit one of the most basic and widely applicable techniques in the literature of differential privacy - the sparse vector technique [Dwork et al., STOC 2009].
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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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. P. Vadhan · 2009
Earlier work this paper cites.
Boosting and differential privacy
C. Dwork, G. N. Rothblum, and S. P. Vadhan · 2010
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Differentially private combinatorial optimization
A. Gupta, K. Ligett, F. McSherry, A. Roth, and K. Talwar · 2010
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A multiplicative weights mechanism for privacy-preserving data analysis
M. Hardt and G. N. Rothblum · 2010
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Differential privacy for the analyst via private equilibrium computation
J. Hsu, A. Roth, and J. Ullman · 2013
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Privacy-preserving public information for sequential games
A. Blum, J. Morgenstern, A. Sharma, and A. Smith · 2015
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On the privacy properties of variants on the sparse vector technique
Y. Chen and A. Machanavajjhala · 2015
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Privacy and truthful equilibrium selection for aggregative games
R. Cummings, M. J. Kearns, A. Roth, and Z. S. Wu · 2015
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Preserving statistical validity in adaptive data analysis
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and A. L. Roth · 2015
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Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
Cited alongside, same era.
Private multiplicative weights beyond linear queries
J. Ullman · 2015
Cited alongside, same era.
Advanced probabilistic couplings for differential privacy
G. Barthe, N. Fong, M. Gaboardi, B. Grégoire, J. Hsu, and P. Strub · 2016
Cited alongside, same era.
Proving differential privacy via probabilistic couplings
G. Barthe, M. Gaboardi, B. Grégoire, J. Hsu, and P. Strub · 2016
Cited alongside, same era.
Locating a small cluster privately
K. Nissim, U. Stemmer, and S. Vadhan · 2016
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Between pure and approximate differential privacy
T. Steinke and J. Ullman · 2016
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Clustering algorithms for the centralized and local models
K. Nissim and U. Stemmer · 2018
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Make up your mind: The price of online queries in differential privacy
M. Bun, T. Steinke, and J. Ullman · 2019
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Privately detecting changes in unknown distributions
R. Cummings, S. Krehbiel, Y. Lut, and W. Zhang · 2019
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An optimal private stochastic-mab algorithm based on optimal private stopping rule
T. Sajed and O. Sheffet · 2019
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Individual privacy accounting via a renyi filter
V. Feldman and T. Zrnic · 2020
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V. Feldman and T. Steinke · 2017
Cited alongside, same era.
Accuracy first: Selecting a differential privacy level for accuracy constrained ERM
K. Ligett, S. Neel, A. Roth, B. Waggoner, and S. Z. Wu · 2017
Cited alongside, same era.
Understanding the sparse vector technique for differential privacy
M. Lyu, D. Su, and N. Li · 2017
Cited alongside, same era.
Model-agnostic private learning
R. Bassily, A. G. Thakurta, and O. D. Thakkar · 2018
Cited alongside, same era.
Personal communication
A. Beimel, K. Nissim, and U. Stemmer
Cited in the paper.
A. Hassidim, H. Kaplan, Y. Mansour, Y. Matias, and U. Stemmer · 2020
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Privately learning thresholds: Closing the exponential gap
H. Kaplan, K. Ligett, Y. Mansour, M. Naor, and U. Stemmer · 2020
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How to find a point in the convex hull privately
H. Kaplan, M. Sharir, and U. Stemmer · 2020
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Privately answering classification queries in the agnostic PAC model
A. Nandi and R. Bassily · 2020
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