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Machine learning models are vulnerable to membership inference attacks in which an adversary aims to predict whether or not a particular sample was contained in the target model's training dataset.
Calibrating Noise to Sensitivity in Private Data Analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 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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Distilling the Knowledge in a Neural Network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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FitNets: Hints for Thin Deep Nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 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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Cross Modal Distillation for Supervision Transfer
Saurabh Gupta, Judy Hoffman, and Jitendra Malik · 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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Sequence-Level Knowledge Distillation
Yoon Kim and Alexander M. Rush · 2016
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Stealing Machine Learning Models via Prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
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Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks
Dingqi Yang, Daqing Zhang, and Bingqing Qu · 2016
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Wide Residual Networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
Nicolas Papernot, Martin Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
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Regularizing Neural Networks by Penalizing Confident Output Distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey E. Hinton · 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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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning
Junho Yim, Donggyu Joo, Ji-Hoon Bae, and Junmo Kim · 2017
Cited alongside, same era.
Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
Sergey Zagoruyko and Nikos Komodakis · 2017
Cited alongside, same era.
CINIC-10 is not ImageNet or CIFAR-10
Luke Nicholas Darlow, Elliot J. Crowley, Antreas Antoniou, and Amos J. Storkey · 2018
Cited alongside, same era.
Born-Again Neural Networks
Tommaso Furlanello, Zachary Chase Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar · 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.
Extracting Training Data from Large Language Models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel · 2020
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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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Let’s Agree to Agree: Neural Networks Share Classification Order on Real Datasets
Guy Hacohen, Leshem Choshen, and Daphna Weinshall · 2020
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Thieves on Sesame Street! Model Extraction of BERT-based APIs
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, and Mohit Iyyer · 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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Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks
Zheng Xu, Yen-Chang Hsu, and Jiawei Huang · 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.
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
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.
Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models
Benjamin Hilprecht, Martin Härterich, and Daniel Bernau · 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.
Certified Robustness to Adversarial Examples with Differential Privacy
Mathias Lécuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
Cited alongside, same era.
Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries
Shadi Rahimian, Tribhuvanesh Orekondy, and Mario Fritz · 2020
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Membership Inference Attacks From First Principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramèr · 2021
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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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Membership Leakage in Label-Only Exposures
Zheng Li and Yang Zhang · 2021
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Membership Privacy for Machine Learning Models Through Knowledge Transfer
Virat Shejwalkar and Amir Houmansadr · 2021
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Systematic Evaluation of Privacy Risks of Machine Learning Models
Liwei Song and Prateek Mittal · 2021
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On the Importance of Difficulty Calibration in Membership Inference Attacks
Lauren Watson, Chuan Guo, Graham Cormode, and Alexandre Sablayrolles · 2021
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Enhanced Membership Inference Attacks against Machine Learning Models
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, and Reza Shokri · 2021
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Membership Inference Attacks Against Recommender Systems
Minxing Zhang, Zhaochun Ren, Zihan Wang, Pengjie Ren, Zhumin Chen, Pengfei Hu, and Yang Zhang · 2021
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Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models
Xinlei He, Zheng Li, Weilin Xu, Cory Cornelius, and Yang Zhang · 2022
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Auditing Membership Leakages of Multi-Exit Networks
Zheng Li, Yiyong Liu, Xinlei He, Ning Yu, Michael Backes, and Yang Zhang · 2022
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