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Attacks that aim to identify the training data of public neural networks represent a severe threat to the privacy of individuals participating in the training data set.
Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
S. L. Warner · 1965
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Noise injection into inputs in back-propagation learning
K. Matsuoka · 1992
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Comments on “Noise injection into inputs in back propagation learning”
Y. Grandvalet and S. Canu · 1995
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Crisp-dm: Towards a standard process model for data mining
R. Wirth and J. Hipp · 2000
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Differential Privacy
C. Dwork · 2006
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Our Data, Ourselves
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor · 2006
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller · 2007
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What can we learn privately?
S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith · 2008
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Genomic privacy and limits of individual detection in a pool
S. Sankararaman, G. Obozinski, M. I. Jordan, and E. Halperin · 2009
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Stochastic gradient descent with differentially private updates
S. Song, K. Chaudhuri, and A. D. Sarwate · 2013
Earlier work this paper cites.
Private Empirical Risk Minimization
R. Bassily, A. Smith, and A. Thakurta · 2014
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The Algorithmic Foundations of Differential Privacy
C. Dwork and A. Roth · 2014
Earlier work this paper cites.
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
U. Erlingsson, V. Pihur, and A. Korolova · 2014
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Privacy in Pharmacogenetics
M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart · 2014
Earlier work this paper cites.
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
Cited alongside, same era.
Deep face recognition
O. M. Parkhi, A. Vedaldi, and A. Zisserman · 2015
Cited alongside, same era.
Privacy-preserving Deep Learning
R. Shokri and V. Shmatikov · 2015
Cited alongside, same era.
Deep graph kernels
P. Yanardag and S. Vishwanathan · 2015
Cited alongside, same era.
Deep Learning with Differential Privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Cited alongside, same era.
Membership Privacy in MicroRNA-based Studies
M. Backes, P. Berrang, M. Humbert, and P. Manoharan · 2016
Cited alongside, same era.
Deep Learning
Membership inference attacks against ML models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Later among the works it cites.
Locally Differentially Private Protocols for Frequency Estimation
T. Wang, J. Blocki, N. Li, and S. Jha · 2017
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The secret sharer
N. Carlini, C. Liu, J. Kos, Ú. Erlingsson, and D. Song · 2018
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Image pixelization with differential privacy
L. Fan · 2018
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Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks
M. Nasr, R. Shokri, and A. Houmansadr · 2018
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Machine learning with membership privacy using adversarial regularization
M. Nasr, R. Shokri, and A. Houmansadr · 2018
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I. Goodfellow, Y. Bengio, and A. Courville · 2016
Cited alongside, same era.
Principled evaluation of differentially private algorithms using dpbench
M. Hay, A. Machanavajjhala, G. Miklau, Y. Chen, and D. Zhang · 2016
Cited alongside, same era.
Stealing machine learning models via prediction apis
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart · 2016
Cited alongside, same era.
Google DeepMind NHS app test broke UK privacy law, 07 2017
BBC News · 2017
Cited alongside, same era.
The Composition Theorem for Differential Privacy
P. Kairouz, S. Oh, and P. Viswanath · 2017
Cited alongside, same era.
Rényi differential privacy
I. Mironov · 2017
Cited alongside, same era.
Membership inference attack against differentially private deep learning model
M. A. Rahman, T. Rahman, R. Laganière, and N. Mohammed · 2018
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Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
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An end-to-end deep learning architecture for graph classification
M. Zhang, Z. Cui, M. Neumann, and Y. Chen · 2018
Later among the works it cites.
An economic analysis of privacy protection and statistical accuracy as social choices
J. M. Abowd and I. M. Schmutte · 2019
Closest in time.
LOGAN: Membership Inference Attacks Against Generative Models
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro · 2019
Closest in time.
Towards practical differentially private convex optimization
R. Iyengar, J. P. Near, D. Song, O. D. Thakkar, A. Thakurta, and L. Wang · 2019
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Evaluating differentially private machine learning in practice
B. Jayaraman and D. Evans · 2019
Closest in time.