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This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility.
On a modification of chebyshev’s inequality and of the error formula of laplace
Sergei Bernstein · 1924
Earlier work this paper cites.
On the number of real roots of a random algebraic equation (iii)
John Edensor Littlewood and Albert Cyril Offord · 1943
Earlier work this paper cites.
On a lemma of littlewood and offord
Paul Erdös · 1945
Earlier work this paper cites.
A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations
Herman Chernoff · 1952
Earlier work this paper cites.
Probability inequalities for sums of bounded random variables
Wassily Hoeffding · 1963
Earlier work this paper cites.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
Earlier work this paper cites.
Inverse mapping of continuous functions using local and global information
Sukhan Lee and Rhee Man Kil · 1994
Earlier work this paper cites.
Neural networks for pattern recognition
Christopher M Bishop · 1995
Earlier work this paper cites.
Health insurance portability and accountability act of 1996
Accountability Act · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Inversion of feedforward neural networks: algorithms and applications
Craig A Jensen, Russell D Reed, Robert J Marks, Mohamed A El-Sharkawi, Jae-Byung Jung, Robert T Miyamoto, Gregory M Anderson, and Christian J Eggen · 1999
Earlier work this paper cites.
Inverting feedforward neural networks using linear and nonlinear programming
Bao-Liang Lu, Hajime Kita, and Yoshikazu Nishikawa · 1999
Earlier work this paper cites.
On the momentum term in gradient descent learning algorithms
Ning Qian · 1999
Earlier work this paper cites.
Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
Earlier work this paper cites.
Distributional and L q {L}^{q} norm inequalities for polynomials over convex bodies in R n {R}^{n}
Anthony Carbery and James Wright · 2001
Earlier work this paper cites.
Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, Eero P Simoncelli, et al · 2004
Earlier work this paper cites.
Observer based iterative neural network model inversion
Annamária R Várkonyi-Kóczy and A Rovid · 2005
Earlier work this paper cites.
Stable signal recovery from incomplete and inaccurate measurements
Emmanuel J Candes, Justin K Romberg, and Terence Tao · 2006
Earlier work this paper cites.
Random symmetric matrices are almost surely nonsingular
Kevin P Costello, Terence Tao, and Van Vu · 2006
Earlier work this paper cites.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Compressed sensing
David L. Donoho · 2006
Earlier work this paper cites.
Differential privacy: A survey of results
Cynthia Dwork · 2008
Earlier work this paper cites.
The differential privacy frontier
Cynthia Dwork · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Approximate sparse recovery: optimizing time and measurements
Anna C Gilbert, Yi Li, Ely Porat, and Martin J Strauss · 2010
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Implementation and benchmarking of perceptual image hash functions
Christoph Zauner · 2010
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A firm foundation for private data analysis
Cynthia Dwork · 2011
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Nearly optimal sparse Fourier transform
Haitham Hassanieh, Piotr Indyk, Dina Katabi, and Eric Price · 2012
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Geert Litjens, Clara I Sánchez, Nadya Timofeeva, Meyke Hermsen, Iris Nagtegaal, Iringo Kovacs, Christina Hulsbergen-Van De Kaa, Peter Bult, Bram Van Ginneken, and Jeroen Van Der Laak · 2016
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Differential privacy preservation for deep auto-encoders: an application of human behavior prediction
NhatHai Phan, Yue Wang, Xintao Wu, and Dejing Dou · 2016
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Learning efficient object detection models with knowledge distillation
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Deviation of polynomials from their expectations and isoperimetry
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A matrix expander chernoff bound
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