Fetching the paper…
Reading the bibliography…
In this paper, we develop new test statistics for private hypothesis testing.
Inverting Modified Matrices
M. A. Woodbury · 1950
Earlier work this paper cites.
Discrete multivariate analysis: Theory and practice, 1975
Y. M. M. Bishop, S. E. Fienberg, and P. W. Holland · 1975
Earlier work this paper cites.
An accessible proof of craig’s theorem in the noncentral case
M. F. D. John G. Reid · 1988
Earlier work this paper cites.
A Course in Large Sample Theory
T. Ferguson · 1996
Earlier work this paper cites.
Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J. V. Pearson, D. A. Stephan, S. F. Nelson, and D. W. Craig · 2008
Earlier work this paper cites.
Differential privacy for clinical trial data: Preliminary evaluations
D. Vu and A. Slavković · 2009
Earlier work this paper cites.
Differential privacy and the risk-utility tradeoff for multi-dimensional contingency tables
S. E. Fienberg, A. Rinaldo, and X. Yang · 2010
Earlier work this paper cites.
A statistical framework for differential privacy
L. Wasserman and S. Zhou · 2010
Earlier work this paper cites.
Privacy-preserving statistical estimation with optimal convergence rates
A. Smith · 2011
Cited alongside, same era.
Differentially private graphical degree sequences and synthetic graphs
V. Karwa and A. Slavković · 2012
Cited alongside, same era.
Privacy-preserving data exploration in genome-wide association studies
A. Johnson and V. Shmatikov · 2013
Cited alongside, same era.
Privacy-preserving data sharing for genome-wide association studies
C. Uhler, A. Slavkovic, and S. E. Fienberg · 2013
Cited alongside, same era.
Scalable privacy-preserving data sharing methodology for genome-wide association studies
F. Yu, S. E. Fienberg, A. B. Slavković, and C. Uhler · 2014
Cited alongside, same era.
Differentially private least squares: Estimation, confidence and rejecting the null hypothesis
Concentrated differential privacy: Simplifications, extensions, and lower bounds
M. Bun and T. Steinke · 2016
Closest in time.
Adaptive learning with robust generalization guarantees
R. Cummings, K. Ligett, K. Nissim, A. Roth, and Z. S. Wu · 2016
Closest in time.
Concentrated differential privacy
C. Dwork and G. N. Rothblum · 2016
Closest in time.
Differentially private chi-squared hypothesis testing: Goodness of fit and independence testing
M. Gaboardi, H. Lim, R. M. Rogers, and S. P. Vadhan · 2016
Closest in time.
Inference using noisy degrees: Differentially private β \beta -model and synthetic graphs
V. Karwa and A. Slavković · 2016
Closest in time.
Max-information, differential privacy, and post-selection hypothesis testing
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
O. Sheffet · 2015
Cited alongside, same era.
Differentially private hypothesis testing, revisited
Y. Wang, J. Lee, and D. Kifer · 2015
Cited alongside, same era.
Algorithmic stability for adaptive data analysis
R. Bassily, K. Nissim, A. D. Smith, T. Steinke, U. Stemmer, and J. Ullman · 2016
Cited alongside, same era.
Our data, ourselves: Privacy via distributed noise generation
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor
Cited in the paper.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith
Cited in the paper.
Preserving statistical validity in adaptive data analysis
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and A. Roth
Cited in the paper.
Generalization in adaptive data analysis and holdout reuse
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and A. Roth
Cited in the paper.
R. Rogers, A. Roth, A. Smith, and O. Thakkar · 2016
Closest in time.
Enabling privacy-preserving {GWASs} in heterogeneous human populations
S. Simmons, C. Sahinalp, and B. Berger · 2016
Closest in time.