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Machine learning (ML) has progressed rapidly during the past decade and the major factor that drives such development is the unprecedented large-scale data.
The Hungarian Method for the Assignment Problem
Harold W Kuhn · 1955
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Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays
Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John V. Pearson, Dietrich A. Stephan, Stanley F. Nelson, and David W. Craig · 2008
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Privacy-preserving Logistic Regression
Kamalika Chaudhuri and Claire Monteleoni · 2009
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Intriguing Properties of Neural Networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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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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Conditional Generative Adversarial Nets
Mehdi Mirza and Simon Osindero · 2014
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Optimal Randomized Classification in Adversarial Settings
Yevgeniy Vorobeychik and Bo Li · 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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Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings
Bo Li and Yevgeniy Vorobeychik · 2015
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Privacy-Preserving Deep Learning
Reza Shokri and Vitaly Shmatikov · 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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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Stealing Machine Learning Models via Prediction APIs
Florian Tramér, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
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walk2friends: Inferring Social Links from Mobility Profiles
Michael Backes, Mathias Humbert, Jun Pang, and Yang Zhang · 2017
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Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini and David Wagner · 2017
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Privacy-Preserving Distributed Linear Regression on High-Dimensional Data
Adrià Gascón, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur, and David Evans · 2017
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Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 2017
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Towards Measuring Membership Privacy
Yunhui Long, Vincent Bindschaedler, and Carl A. Gunter · 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
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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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Machine Learning Models that Remember Too Much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov · 2017
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Ensemble Adversarial Training: Attacks and Defenses
With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning
Bolun Wang, Yuanshun Yao, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao · 2018
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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Weilin Xu, David Evans, and Yanjun Qi · 2018
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Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
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Tagvisor: A Privacy Advisor for Sharing Hashtags
Yang Zhang, Mathias Humbert, Tahleen Rahman, Cheng-Te Li, Jun Pang, and Michael Backes · 2018
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MBeacon: Privacy-Preserving Beacons for DNA Methylation Data
Inken Hagestedt, Yang Zhang, Mathias Humbert, Pascal Berrang, Haixu Tang, XiaoFeng Wang, and Michael Backes · 2019
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Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Cited alongside, same era.
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Yuanshun Yao, Bimal Viswanath, Jenna Cryan, Haitao Zheng, and Ben Y. Zhao · 2017
Cited alongside, same era.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Anish Athalye, Nicholas Carlini, and David A. Wagner · 2018
Cited alongside, same era.
Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations
Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov · 2018
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LEMNA: Explaining Deep Learning based Security Applications
Wenbo Guo, Dongliang Mu, Jun Xu, Purui Su, and Gang Wang abd Xinyu Xing · 2018
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Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li · 2018
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Understanding Membership Inferences on Well-Generalized Learning Models
Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A. Gunter, and Kai Chen · 2018
Cited alongside, same era.
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2019
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MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong · 2019
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How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNN
Zheng Li, Chengyu Hu, Yang Zhang, and Shanqing Guo · 2019
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Exploiting Unintended Feature Leakage in Collaborative Learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
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Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
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Knockoff Nets: Stealing Functionality of Black-Box Models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
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Under the Hood of Membership Inference Attacks on Aggregate Location Time-Series
Apostolos Pyrgelis, Carmela Troncoso, and Emiliano De Cristofaro · 2019
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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes · 2019
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Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning
Mohammad Yaghini, Bogdan Kulynych, and Carmela Troncoso · 2019
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Diversity-Sensitive Conditional Generative Adversarial Networks
Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, and Honglak Lee · 2019
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Cost-Sensitive Robustness against Adversarial Examples
Xiao Zhang and David Evans · 2019
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Towards Plausible Graph Anonymization
Yang Zhang, Mathias Humbert, Bartlomiej Surma, Praveen Manoharan, Jilles Vreeken, and Michael Backes · 2020
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