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We introduce an analytical framework to quantify the changes in a machine learning algorithm's output distribution following the inclusion of a few data points in its training set, a notion we define as leave-one-out distinguishability (LOOD).
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Solomon Kullback and Richard A Leibler · 1951
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Lloyd S Shapley et al · 1953
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Calibrating noise to sensitivity in private data analysis
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Gaussian processes for machine learning
Christopher KI Williams and Carl Edward Rasmussen · 2006
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Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
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The algorithmic foundations of differential privacy
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Deep neural networks as gaussian processes
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Rényi differential privacy
Ilya Mironov · 2017
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Deep information propagation
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Membership inference attacks against machine learning models
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei W Koh, and Percy S Liang · 2017
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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Bayesian deep convolutional networks with many channels are gaussian processes
Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Greg Yang, Jiri Hron, Daniel A Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2018
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Jinshuo Dong, Aaron Roth, and Weijie J Su · 2019
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Soufiane Hayou, Arnaud Doucet, and Judith Rousseau · 2019
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On the accuracy of influence functions for measuring group effects
Pang Wei W Koh, Kai-Siang Ang, Hubert Teo, and Percy S Liang · 2019
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Wide feedforward or recurrent neural networks of any architecture are gaussian processes
Greg Yang · 2019
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Counterfactual memorization in neural language models
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, and Nicholas Carlini · 2021
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Reconstructing training data with informed adversaries
Borja Balle, Giovanni Cherubin, and Jamie Hayes · 2022
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Bounding training data reconstruction in private (deep) learning
Chuan Guo, Brian Karrer, Kamalika Chaudhuri, and Laurens van der Maaten · 2022
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Reconstructing training data from trained neural networks
Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, and Michal Irani · 2022
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Efficient dataset distillation using random feature approximation
Noel Loo, Ramin Hasani, Alexander Amini, and Daniela Rus · 2022
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Does learning require memorization? a short tale about a long tail
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Auditing differentially private machine learning: How private is private sgd?
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Towards nngp-guided neural architecture search, 2020
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Feature learning and signal propagation in deep neural networks
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Canary in a coalmine: Better membership inference with ensembled adversarial queries
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Enhanced membership inference attacks against machine learning models
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Extracting training data from diffusion models
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
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Tight auditing of differentially private machine learning
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Mnemonist: locating model parameters that memorize training examples
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Low-cost high-power membership inference attacks
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Privacy auditing with one (1) training run
Thomas Steinke, Milad Nasr, and Matthew Jagielski · 2024
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