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In a membership inference attack, an attacker aims to infer whether a data sample is in a target classifier's training dataset or not.
Evaluating Differentially Private Machine Learning in Practice. In Proceedings of the 2014 USENIX Security Symposium (USENIX Security) . USENIX, 1895–1912
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walk2friends: Inferring Social Links from Mobility Profiles. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 1943–1957
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Back to the Drawing Board: Revisiting the Design of Optimal Location Privacy-preserving Mechanisms. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 1943–1957
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Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays
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Website Fingerprinting: Attacking Popular Privacy Enhancing Technologies with the Multinomial Naive-Bayes Classifier. In Proceedings of the 2009 ACM Cloud Computing Security Workshop (CCSW) . ACM, 31–41
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Differentially Private Empirical Risk Minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. 2011 · 2011
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Website Fingerprinting in Onion Routing Based Anonymization Networks. In Proceedings of the 2011 Workshop on Privacy in the Electronic Society (WPES) . ACM, 103–114
Andriy Panchenko, Lukas Niessen, Andreas Zinnen, and Thomas Engel. 2011 · 2011
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Touching from a Distance: Website Fingerprinting Attacks and Defenses. In Proceedings of the 2012 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 605–616
Xiang Cai, Xin Cheng Zhang, Brijesh Joshi, and Rob Johnson. 2012 · 2012
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You Are What You Like! Information Leakage Through Users’ Interests. In Proceedings of the 2012 Network and Distributed System Security Symposium (NDSS) . Internet Society
Abdelberi Chaabane, Gergely Acs, and Mohamed Ali Kaafar. 2012 · 2012
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Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression. In Proceedings of the 2012 Annual Conference on Learning Theory (COLT) . JMLR, 1–25
Daniel Kifer, Adam Smith, and Abhradeep Thakurta. 2012 · 2012
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On the Feasibility of Internet-Scale Author Identification. In Proceedings of the 2012 IEEE Symposium on Security and Privacy (S&P) . IEEE, 300–314
Arvind Narayanan, Hristo S. Paskov, Neil Zhenqiang Gong, John Bethencourt, Emil Stefanov, Eui Chul Richard Shin, and Dawn Song. 2012 · 2012
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Cross-VM Side Channels and Their Use to Extract Private Keys. In Proceedings of the 2012 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 305–316
Yinqian Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart. 2012 · 2012
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Giuseppe Ateniese, Giovanni Felici, Luigi V. Mancini, Angelo Spognardi, Antonio Villani, and Domenico Vitali. 2013 · 2013
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Stochastic Gradient Descent with Differentially Private Updates. In Proceedings of the 2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP) . IEEE, 245–248
Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate. 2013 · 2013
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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 · 2013
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Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. In Proceedings of the 2014 Annual Symposium on Foundations of Computer Science (FOCS) . IEEE, 464–473
Raef Bassily, Adam Smith, and Abhradeep Thakurta. 2014 · 2014
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Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. In Proceedings of the 2014 USENIX Security Symposium (USENIX Security) . USENIX, 17–32
Matt Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart. 2014 · 2014
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Generative Adversarial Nets. In Proceedings of the 2014 Annual Conference on Neural Information Processing Systems (NIPS) . NIPS
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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A Critical Evaluation of Website Fingerprinting Attacks. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 263–274
Marc Juarez, Sadia Afroz, Gunes Acar, Claudia Diaz, and Rachel Greenstadt. 2014 · 2014
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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 · 2014
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Effective Attacks and Provable Defenses for Website Fingerprinting. In Proceedings of the 2014 USENIX Security Symposium (USENIX Security) . USENIX, 143–157
Tao Wang, Xiang Cai, Rishab Nithyanand, Rob Johnson, and Ian Goldberg. 2014 · 2014
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Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures. In Proceedings of the 2015 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 1322–1333
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015 · 2015
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Explaining and Harnessing Adversarial Examples. In Proceedings of the 2015 International Conference on Learning Representations (ICLR)
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
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Privacy-Preserving Deep Learning. In Proceedings of the 2015 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 1310–1321
Reza Shokri and Vitaly Shmatikov. 2015 · 2015
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Deep Learning with Differential Privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 308–318
Martin Abadi, Andy Chu, Ian Goodfellow, Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Cited alongside, same era.
Membership Privacy in MicroRNA-based Studies. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 319–330
Michael Backes, Pascal Berrang, Mathias Humbert, and Praveen Manoharan. 2016 · 2016
Cited alongside, same era.
You are Who You Know and How You Behave: Attribute Inference Attacks via Users’ Social Friends and Behaviors. In Proceedings of the 2016 USENIX Security Symposium (USENIX Security) . USENIX, 979–995
Neil Zhenqiang Gong and Bin Liu. 2016 · 2016
Cited alongside, same era.
Adversarial Examples in the Physical World
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016 · 2016
Cited alongside, same era.
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 · 2018
Later among the works it cites.
Towards Deep Learning Models Resistant to Adversarial Attacks. In Proceedings of the 2018 International Conference on Learning Representations (ICLR)
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018
Later among the works it cites.
Adversarial Binaries for Authorship Identification
Xiaozhu Meng, Barton P Miller, and Somesh Jha. 2018 · 2018
Later among the works it cites.
Machine Learning with Membership Privacy using Adversarial Regularization. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2018 · 2018
Later among the works it cites.
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Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. 2016 · 2016
Cited alongside, same era.
Deepfool: A Simple and Accurate Method to Fool Deep Neural Networks. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2574–2582
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2016 · 2016
Cited alongside, same era.
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow. 2016a · 2016
Cited alongside, same era.
The Limitations of Deep Learning in Adversarial Settings. In Proceedings of the 2016 IEEE European Symposium on Security and Privacy (Euro S&P) . IEEE, 372–387
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami. 2016b · 2016
Cited alongside, same era.
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. In Proceedings of the 2016 IEEE Symposium on Security and Privacy (S&P) . IEEE, 582–597
Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. 2016c · 2016
Cited alongside, same era.
Stealing Machine Learning Models via Prediction APIs. In Proceedings of the 2016 USENIX Security Symposium (USENIX Security) . USENIX, 601–618
Florian Tramér, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart. 2016 · 2016
Cited alongside, same era.
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification. In Proceedings of the 2017 Annual Computer Security Applications Conference (ACSAC) . ACM, 278–287
Xiaoyu Cao and Neil Zhenqiang Gong. 2017 · 2017
Cited alongside, same era.
Towards Evaluating the Robustness of Neural Networks. In Proceedings of the 2017 IEEE Symposium on Security and Privacy (S&P) . IEEE, 39–57
Nicholas Carlini and David Wagner. 2017 · 2017
Cited alongside, same era.
Towards Reverse-Engineering Black-Box Neural Networks. In Proceedings of the 2018 International Conference on Learning Representations (ICLR)
Seong Joon Oh, Max Augustin, Bernt Schiele, and Mario Fritz. 2018 · 2018
Later among the works it cites.
SoK: Towards the Science of Security and Privacy in Machine Learning. In Proceedings of the 2018 IEEE European Symposium on Security and Privacy (Euro S&P) . IEEE
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman. 2018 · 2018
Later among the works it cites.
Knock Knock, Who’s There? Membership Inference on Aggregate Location Data. In Proceedings of the 2018 Network and Distributed System Security Symposium (NDSS) . Internet Society
Apostolos Pyrgelis, Carmela Troncoso, and Emiliano De Cristofaro. 2018 · 2018
Later among the works it cites.
Stealing Hyperparameters in Machine Learning. In Proceedings of the 2018 IEEE Symposium on Security and Privacy (S&P) . IEEE
Binghui Wang and Neil Zhenqiang Gong. 2018 · 2018
Later among the works it cites.
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. In Proceedings of the 2018 Network and Distributed System Security Symposium (NDSS) . Internet Society
Weilin Xu, David Evans, and Yanjun Qi. 2018 · 2018
Later among the works it cites.
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. In Proceedings of the 2018 IEEE Computer Security Foundations Symposium (CSF) . IEEE
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018 · 2018
Later among the works it cites.
Tagvisor: A Privacy Advisor for Sharing Hashtags. In Proceedings of the 2018 Web Conference (WWW) . ACM, 287–296
Yang Zhang, Mathias Humbert, Tahleen Rahman, Cheng-Te Li, Jun Pang, and Michael Backes. 2018 · 2018
Later among the works it cites.
MBeacon: Privacy-Preserving Beacons for DNA Methylation Data. In Proceedings of the 2019 Network and Distributed System Security Symposium (NDSS) . Internet Society
Inken Hagestedt, Yang Zhang, Mathias Humbert, Pascal Berrang, Haixu Tang, XiaoFeng Wang, and Michael Backes. 2019 · 2019
Closest in time.
LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro. 2019 · 2019
Closest in time.
Towards Practical Differentially Private Convex Optimization. In Proceedings of the 2019 IEEE Symposium on Security and Privacy (S&P) . IEEE
Roger Iyengar, Joseph P. Near, Dawn Xiaodong Song, Om Dipakbhai Thakkar, Abhradeep Thakurta, and Lun Wang. 2019 · 2019
Closest in time.
Jinyuan Jia and Neil Zhenqiang Gong. 2019 · 2019
Closest in time.
Exploiting Unintended Feature Leakage in Collaborative Learning. In Proceedings of the 2019 IEEE Symposium on Security and Privacy (S&P) . IEEE
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019 · 2019
Closest in time.
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. In Proceedings of the 2019 IEEE Symposium on Security and Privacy (S&P) . IEEE
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2019 · 2019
Closest in time.
Under the Hood of Membership Inference Attacks on Aggregate Location Time-Series
Apostolos Pyrgelis, Carmela Troncoso, and Emiliano De Cristofaro. 2019 · 2019
Closest in time.
Misleading Authorship Attribution of Source Code using Adversarial Learning. In Proceedings of the 2019 USENIX Security Symposium (USENIX Security) . USENIX, 479–496
Erwin Quiring, Alwin Maier, and Konrad Rieck. 2019 · 2019
Closest in time.
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, and Yang Zhang. 2019a · 2019
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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In Proceedings of the 2019 Network and Distributed System Security Symposium (NDSS) . Internet Society
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes. 2019b · 2019
Closest in time.
Privacy Risks of Securing Machine Learning Models against Adversarial Examples. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM
Liwei Song, Reza Shokri, and Prateek Mittal. 2019 · 2019
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Differentially Private Model Publishing for Deep Learning. In Proceedings of the 2019 IEEE Symposium on Security and Privacy (S&P) . IEEE
Lei Yu, Ling Liu, Calton Pu, Mehmet Emre Gursoy, and Stacey Truex. 2019 · 2019
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Statistical Privacy for Streaming Traffic. In Proceedings of the 2019 Network and Distributed System Security Symposium (NDSS) . Internet Society
Xiaokuan Zhang, Jihun Hamm, Michael K. Reiter, and Yinqian Zhang. 2019 · 2019
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