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Models can expose sensitive information about their training data.
Inference and Missing Data
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Missing Data Imputation Using Statistical and Machine Learning Methods in a Real Breast Cancer Problem
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Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. In USENIX Security Symposium
Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart. 2014 · 2014
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Membership Inference Attacks Against Machine Learning Models. In IEEE Symposium on Security and Privacy
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
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Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. In IEEE Computer Security Foundations Symposium
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The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. In USENIX Security Symposium
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Jinshuo Dong, Aaron Roth, and Weijie J Su. 2019 · 2019
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Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers
Giuseppe Ateniese, Luigi Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali, and Giovanni Felici. 2015 · 2015
Cited alongside, same era.
Data imputation via evolutionary computation, clustering and a neural network
Chandan Gautam and Vadlamani Ravi. 2015 · 2015
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Deep Learning with Differential Privacy. In ACM Conference on Computer and Communications Security
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Cited alongside, same era.
Statistical inference considered harmful
Frank McSherry. 2016 · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune. 2016 · 2016
Cited alongside, same era.
A Methodology for Formalizing Model-Inversion Attacks. In IEEE Computer Security Foundations Symposium
Xi Wu, Matthew Fredrikson, Somesh Jha, and Jeffrey F Naughton. 2016 · 2016
Cited alongside, same era.
From Predictive Methods to Missing Data Imputation: An Optimization Approach
Dimitris Bertsimas, Colin Pawlowski, and Ying Daisy Zhuo. 2017 · 2017
Cited alongside, same era.
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. 2020 · 2020
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Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?
Sorami Hisamoto, Matt Post, and Kevin Duh. 2020 · 2020
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The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song. 2020 · 2020
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Revisiting Membership Inference Under Realistic Assumptions. In Proceedings on Privacy Enhancing Technologies
Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, and David Evans. 2021 · 2021
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Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?
Eric Lehman, Sarthak Jain, Karl Pichotta, Yoav Goldberg, and Byron C Wallace. 2021 · 2021
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ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models
Yugeng Liu, Rui Wen, Xinlei He, Ahmed Salem, Zhikun Zhang, Michael Backes, Emiliano De Cristofaro, Mario Fritz, and Yang Zhang. 2021 · 2021
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On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models
Benjamin Zi Hao Zhao, Aviral Agrawal, Catisha Coburn, Hassan Jameel Asghar, Raghav Bhaskar, Mohamed Ali Kâafar, Darren Webb, and Peter Dickinson. 2021 · 2021
Later among the works it cites.
Shagufta Mehnaz, Sayanton V. Dibbo, Ehsanul Kabir, Ninghui Li, and Elisa Bertino. 2022 · 2022
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