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Consider a database of $n$ people, each represented by a bit-string of length $d$ corresponding to the setting of $d$ binary attributes.
Résumé de la théorie métrique des produits tensoriels topologiques
A. Grothendieck · 1953
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An algorithm for quadratic programming
M. Frank and P. Wolfe · 1956
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Absolutely summing operators in _ \_ { p p }-spaces and their applications
J. Lindenstrauss and A. Pełczyński · 1968
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The ellipsoid method and its consequences in combinatorial optimization
M. Grötschel, L. Lovász, and A. Schrijver · 1981
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An elementary proof of a theorem of Johnson and Lindenstrauss
S. Dasgupta and A. Gupta · 2003
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Revealing information while preserving privacy
I. Dinur and K. Nissim · 2003
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Approximating the cut-norm via Grothendieck’s inequality
N. Alon and A. Naor · 2004
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Privacy-preserving datamining on vertically partitioned databases
C. Dwork and K. Nissim · 2004
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Our data, ourselves: Privacy via distributed noise generation, 2006
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor · 2006
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. Mcsherry, K. Nissim, and A. Smith · 2006
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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
B. Barak, K. Chaudhuri, C. Dwork, S. Kale, F. McSherry, and K. Talwar · 2007
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Linear Equations Modulo 2 and the L1 Diameter of Convex Bodies
S. Khot and A. Naor · 2007
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A learning theory approach to non-interactive database privacy
A. Blum, K. Ligett, and A. Roth · 2008
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Concentration of Measure for the Analysis of Randomized Algorithms
D. P. Dubhashi and A. Panconesi · 2009
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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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Coresets, sparse greedy approximation, and the frank-wolfe algorithm
Interactive privacy via the median mechanism
A. Roth and T. Roughgarden · 2010
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Privately releasing conjunctions and the statistical query barrier
A. Gupta, M. Hardt, A. Roth, and J. Ullman · 2011
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Pcps and the hardness of generating private synthetic data
J. Ullman and S. Vadhan · 2011
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Unconditional differentially private mechanisms for linear queries
A. Bhaskara, D. Dadush, R. Krishnaswamy, and K. Talwar · 2012
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Submodular functions are noise stable
M. Cheraghchi, A. Klivans, P. Kothari, and H. K. Lee · 2012
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A simple and practical algorithm for differentially private data release
M. Hardt, K. Ligett, and F. McSherry · 2012
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K. Clarkson · 2010
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Boosting and differential privacy
C. Dwork, G. N. Rothblum, and S. Vadhan · 2010
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A multiplicative weights mechanism for privacy-preserving data analysis
M. Hardt and G. Rothblum · 2010
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On the geometry of differential privacy
M. Hardt and K. Talwar · 2010
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The price of privately releasing contingency tables and the spectra of random matrices with correlated rows
S. Kasiviswanathan, M. Rudelson, A. Smith, and J. Ullman · 2010
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Private data release via learning thresholds
M. Hardt, G. N. Rothblum, and R. A. Servedio · 2012
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Faster algorithms for privately releasing marginals
J. Thaler, J. Ullman, and S. P. Vadhan · 2012
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Faster private release of marginals on small databases
K. Chandrasekaran, J. Thaler, J. Ullman, and A. Wan · 2013
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The geometry of differential privacy: the sparse and approximate cases
A. Nikolov, K. Talwar, and L. Zhang · 2013
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