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Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model.
The jackknife, the bootstrap and other resampling plans
1982
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Algorithms for clustering data
1988
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Network information criterion-determining the number of hidden units for an artificial neural network model
1994
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Linear hinge loss and average margin
1999
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Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping
2001
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Stability and generalization
2002
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Combining dependent p-values
2002
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A survey of clustering data mining techniques
2006
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Differential privacy in the 40th international colloquium on automata
2006
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A fast learning algorithm for deep belief nets
2006
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The local mixing problem
2006
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Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
2008
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Protecting aggregate genomic data
2008
Cited alongside, same era.
A practical differentially private random decision tree classifier
2009
Cited alongside, same era.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
2009
Cited alongside, same era.
Mnist handwritten digit database
2010
Cited alongside, same era.
Differential privacy: On the trade-off between utility and information leakage
2011
Cited alongside, same era.
Personal privacy vs population privacy: learning to attack anonymization
2011
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
2016
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Deep learning with differential privacy
2016
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Delving into transferable adversarial examples and black-box attacks
2016
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Membership inference attacks against machine learning models
2016
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Stealing machine learning models via prediction apis
2016
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2012
Cited alongside, same era.
Privacy-preserving data exploration in genome-wide association studies
2013
Cited alongside, same era.
UCI machine learning repository, 2013
2013
Cited alongside, same era.
Intriguing properties of neural networks
2013
Cited alongside, same era.
Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
2014
Cited alongside, same era.
Privacy-preserving deep learning
2015
Cited alongside, same era.
2017
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Adversarial examples for generative models
2017
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Towards measuring membership privacy
2017
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Learning differentially private language models without losing accuracy
2017
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Machine learning models that remember too much
2017
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The unintended consequences of overfitting: Training data inference attacks
2017
Later among the works it cites.