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Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to infer whether an input sample was used to train the model.
Calibrating Noise to Sensitivity in Private Data Analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Differentially Private Empirical Risk Minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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The Algorithmic Foundations of Differential Privacy
Cynthia Dwork and Aaron Roth · 2014
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Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing
Matt Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart · 2014
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Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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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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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2015
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Deep Learning with Differential Privacy
Martin Abadi, Andy Chu, Ian Goodfellow, Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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You are Who You Know and How You Behave: Attribute Inference Attacks via Users’ Social Friends and Behaviors
Neil Zhenqiang Gong and Bin Liu · 2016
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Differential Privacy: From Theory to Practice
Ninghui Li, Min Lyu, Dong Su, and Weining Yang · 2016
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The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2016
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Rethinking the Inception Architecture for Computer Vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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Wide Residual Networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Badnets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Grag · 2017
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Practical Black-Box Attacks Against Machine Learning
Nicolas Papernot, Patrick D. McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
Earlier work this paper cites.
Membership Inference Attacks Against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
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Machine Learning with Membership Privacy using Adversarial Regularization
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
Cited alongside, same era.
SoK: Towards the Science of Security and Privacy in Machine Learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2018
Cited alongside, same era.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Places: A 10 Million Image Database for Scene Recognition
Bolei Zhou, Àgata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2018
GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz · 2020
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HopSkipJumpAttack: A Query-Efficient Decision-Based Attack
Jianbo Chen, Michael I. Jordan, and Martin J. Wainwright · 2020
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RandAugment: Practical Automated Data Augmentation with a Reduced Search Space
Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, and Quoc Le · 2020
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Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference
Klas Leino and Matt Fredrikson · 2020
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QEBA: Query-Efficient Boundary-Based Blackbox Attack
Huichen Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, and Bo Li · 2020
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Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
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Cited alongside, same era.
LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2019
Cited alongside, same era.
Towards Practical Differentially Private Convex Optimization
Roger Iyengar, Joseph P. Near, Dawn Xiaodong Song, Om Dipakbhai Thakkar, Abhradeep Thakurta, and Lun Wang · 2019
Cited alongside, same era.
Evaluating Differentially Private Machine Learning in Practice
Bargav Jayaraman and David Evans · 2019
Cited alongside, same era.
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong · 2019
Cited alongside, same era.
Exploiting Unintended Feature Leakage in Collaborative Learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
Cited alongside, same era.
When Does Label Smoothing Help?
Rafael Müller, Simon Kornblith, and Geoffrey E. Hinton · 2019
Cited alongside, same era.
Yunfei Liu, Xingjun Ma, James Bailey, and Feng Lu · 2020
Later among the works it cites.
Hidden Trigger Backdoor Attacks
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash · 2020
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Information Leakage in Embedding Models
Congzheng Song and Ananth Raghunathan · 2020
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Overlearning Reveals Sensitive Attributes
Congzheng Song and Vitaly Shmatikov · 2020
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Clean-Label Backdoor Attacks on Video Recognition Models
Shihao Zhao, Xingjun Ma, Xiang Zheng, James Bailey, Jingjing Chen, and Yu-Gang Jiang · 2020
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Label-Only Membership Inference Attacks
Christopher A. Choquette Choo, Florian Tramèr, Nicholas Carlini, and Nicolas Papernot · 2021
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Node-Level Membership Inference Attacks Against Graph Neural Networks
Xinlei He, Rui Wen, Yixin Wu, Michael Backes, Yun Shen, and Yang Zhang · 2021
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Quantifying and Mitigating Privacy Risks of Contrastive Learning
Xinlei He and Yang Zhang · 2021
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When Does Data Augmentation Help With Membership Inference Attacks?
Yigitcan Kaya and Tudor Dumitras · 2021
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Membership Inference Attacks and Defenses in Classification Models
Jiacheng Li, Ninghui Li, and Bruno Ribeiro · 2021
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Membership Leakage in Label-Only Exposures
Zheng Li and Yang Zhang · 2021
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Membership Inference Attack on Graph Neural Networks
Iyiola E. Olatunji, Wolfgang Nejdl, and Megha Khosla · 2021
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Systematic Evaluation of Privacy Risks of Machine Learning Models
Liwei Song and Prateek Mittal · 2021
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How Does Data Augmentation Affect Privacy in Machine Learning?
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu · 2021
Later among the works it cites.