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Property inference attacks consider an adversary who has access to the trained model and tries to extract some global statistics of the training data.
“Towards Privacy and Security of Deep Learning Systems: A Survey”, 2019
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Bryan Klimt and Yiming Yang · 2004
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“Calibrating Noise to Sensitivity in Private Data Analysis”
Cynthia Dwork, Frank McSherry, Kobbi Nissim and Adam. Smith · 2006
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“UCI machine learning repository, 2010”
Andrew Frank and Arthur Asuncion · 2011
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“Poisoning attacks against support vector machines”
Battista Biggio, Blaine Nelson and Pavel Laskov · 2012
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Giuseppe Ateniese et al · 2013
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“Explaining and harnessing adversarial examples”
Ian Goodfellow, Jonathon Shlens and Christian Szegedy · 2014
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“Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers”
Giuseppe Ateniese et al · 2015
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“Model inversion attacks that exploit confidence information and basic countermeasures”
Matt Fredrikson, Somesh Jha and Thomas Ristenpart · 2015
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“Agnostic estimation of mean and covariance”
Kevin Lai, Anup Rao and Santosh Vempala · 2016
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“Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning”, 2017
Xinyun Chen et al · 2017
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“Towards deep learning models resistant to adversarial attacks”
Aleksander Madry et al · 2017
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“Blockwise p-tampering attacks on cryptographic primitives, extractors, and learners”
Saeed Mahloujifar and Mohammad Mahmoody · 2017
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“Membership inference attacks against machine learning models”
Reza Shokri, Marco Stronati, Congzheng Song and Vitaly Shmatikov · 2017
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“Certified defenses for data poisoning attacks”
Jacob Steinhardt, Pang Koh and Percy Liang · 2017
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“The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks”
Nicholas Carlini et al · 2018
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“Property Inference Attacks on Fully Connected Neural Networks Using Permutation Invariant Representations”
Karan Ganju et al · 2018
Cited alongside, same era.
“Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations”
Karan Ganju et al · 2018
Cited alongside, same era.
“Stronger data poisoning attacks break data sanitization defenses”
Pang Koh, Jacob Steinhardt and Percy Liang · 2018
Cited alongside, same era.
“Learning under
Saeed Mahloujifar, Dimitrios Diochnos and Mohammad Mahmoody · 2018
Cited alongside, same era.
“Machine learning with membership privacy using adversarial regularization”
Milad Nasr, Reza Shokri and Amir Houmansadr · 2018
Cited alongside, same era.
“Data poisoning attacks in multi-party learning”
Saeed Mahloujifar, Mohammad Mahmoody and Ameer Mohammed · 2019
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“Exploiting Unintended Feature Leakage in Collaborative Learning”
Luca Melis, Congzheng Song, Emiliano De and Vitaly Shmatikov · 2019
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“Comprehensive privacy analysis of deep learning”
Milad Nasr, Reza Shokri and Amir Houmansadr · 2019
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“White-box vs Black-box: Bayes Optimal Strategies for Membership Inference”
Alexandre Sablayrolles et al · 2019
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“Auditing data provenance in text-generation models”
Congzheng Song and Vitaly Shmatikov · 2019
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“Privacy risks of securing machine learning models against adversarial examples”
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Adarsh Prasad, Arun Suggala, Sivaraman Balakrishnan and Pradeep Ravikumar · 2018
Cited alongside, same era.
“Poison frogs! targeted clean-label poisoning attacks on neural networks”
Ali Shafahi et al · 2018
Cited alongside, same era.
“Stealing hyperparameters in machine learning”
Binghui Wang and Neil Gong · 2018
Cited alongside, same era.
“Analyzing federated learning through an adversarial lens”
Arjun Bhagoji, Supriyo Chakraborty, Prateek Mittal and Seraphin Calo · 2019
Cited alongside, same era.
“Robust estimators in high-dimensions without the computational intractability”
Ilias Diakonikolas et al · 2019
Cited alongside, same era.
“Sever: A robust meta-algorithm for stochastic optimization”
Ilias Diakonikolas et al · 2019
Cited alongside, same era.
“Lower bounds for adversarially robust pac learning”
Dimitrios Diochnos, Saeed Mahloujifar and Mohammad Mahmoody · 2019
Cited alongside, same era.
Liwei Song, Reza Shokri and Prateek Mittal · 2019
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“3 ways to train a secure machine learning model” Accessed: 2020-03-04, https://www.ericsson.com/en/blog/2020/2/training-a-machine-learning-model
2020
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“How to backdoor federated learning”
Eugene Bagdasaryan et al · 2020
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“Label-Only Membership Inference Attacks”
Christopher Choo, Florian Tramer, Nicholas Carlini and Nicolas Papernot · 2020
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“Obliviousness Makes Poisoning Adversaries Weaker”
Sanjam Garg et al · 2020
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“Data Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses”
Micah Goldblum et al · 2020
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“Auditing Differentially Private Machine Learning: How Private is Private SGD?”
Matthew Jagielski, Jonathan Ullman and Alina Oprea · 2020
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“Learning under p-tampering poisoning attacks”
Saeed Mahloujifar, Dimitrios Diochnos and Mohammad Mahmoody · 2020
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“Privacy-preserving Collaborative Machine Learning” Accessed: 2020-03-04, https://medium.com/sap-machine-learning-research/privacy-preserving-collaborative-machine-learning-35236870cd43
2020
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“Systematic Evaluation of Privacy Risks of Machine Learning Models”
Liwei Song and Prateek Mittal · 2020
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“Model-Targeted Poisoning Attacks: Provable Convergence and Certified Bounds”
Fnu Suya, Saeed Mahloujifar, David Evans and Yuan Tian · 2020
Later among the works it cites.