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Many methods in differentially private model training rely on computing the similarity between a query point (such as public or synthetic data) and private data.
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Lectures on discrete geometry , volume 212 of Graduate texts in mathematics
Jirí Matousek · 2002
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Learning with kernels: support vector machines, regularization, optimization, and beyond
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Kernel methods for pattern analysis
John Shawe-Taylor, Nello Cristianini, et al · 2004
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Nearest-neighbor-preserving embeddings
Piotr Indyk and Assaf Naor · 2007
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Kernel methods in machine learning
Thomas Hofmann, Bernhard Schölkopf, and Alexander J Smola · 2008
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The fast johnson–lindenstrauss transform and approximate nearest neighbors
Nir Ailon and Bernard Chazelle · 2009
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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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Private coresets
Dan Feldman, Amos Fiat, Haim Kaplan, and Kobbi Nissim · 2009
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Random projections for k k -means clustering
Christos Boutsidis, Anastasios Zouzias, and Petros Drineas · 2010
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Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 2010
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On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
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The johnson-lindenstrauss transform itself preserves differential privacy
Jeremiah Blocki, Avrim Blum, Anupam Datta, and Or Sheffet · 2012
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Iterative constructions and private data release
Anupam Gupta, Aaron Roth, and Jonathan R. Ullman · 2012
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A learning theory approach to noninteractive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2013
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Differential privacy for functions and functional data
Rob Hall, Alessandro Rinaldo, and Larry Wasserman · 2013
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Bounds on the sample complexity for private learning and private data release
Amos Beimel, Hai Brenner, Shiva Prasad Kasiviswanathan, and Kobbi Nissim · 2014
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Exploiting metric structure for efficient private query release
Zhiyi Huang and Aaron Roth · 2014
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Faster algorithms via approximation theory
Sushant Sachdeva and Nisheeth K. Vishnoi · 2014
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Dimensionality reduction for k-means clustering and low rank approximation
Michael B Cohen, Sam Elder, Cameron Musco, Christopher Musco, and Madalina Persu · 2015
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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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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Differentially private data releasing for smooth queries
Ziteng Wang, Chi Jin, Kai Fan, Jiaqi Zhang, Junliang Huang, Yiqiao Zhong, and Liwei Wang · 2016
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto · 2021
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Randomized dimensionality reduction for facility location and single-linkage clustering
Shyam Narayanan, Sandeep Silwal, Piotr Indyk, and Or Zamir · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Privately learning subspaces
Vikrant Singhal and Thomas Steinke · 2021
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al · 2021
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Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
The bernstein mechanism: Function release under differential privacy
Francesco Aldà and Benjamin I. P. Rubinstein · 2017
Cited alongside, same era.
Efficient density evaluation for smooth kernels
Arturs Backurs, Moses Charikar, Piotr Indyk, and Paris Siminelakis · 2018
Cited alongside, same era.
Space and time efficient kernel density estimation in high dimensions
Arturs Backurs, Piotr Indyk, and Tal Wagner · 2019
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Oblivious dimension reduction for k-means: beyond subspaces and the johnson-lindenstrauss lemma
Luca Becchetti, Marc Bury, Vincent Cohen-Addad, Fabrizio Grandoni, and Chris Schwiegelshohn · 2019
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
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Performance of johnson-lindenstrauss transform for k -means and k -medians clustering
Konstantin Makarychev, Yury Makarychev, and Ilya P. Razenshteyn · 2019
Cited alongside, same era.
Subquadratic algorithms for kernel matrices via kernel density estimation
Ainesh Bakshi, Piotr Indyk, Praneeth Kacham, Sandeep Silwal, and Samson Zhou · 2022
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Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
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Moses Charikar and Erik Waingarten · 2022
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Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 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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Faster linear algebra for distance matrices
Piotr Indyk and Sandeep Silwal · 2022
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Truth serum: Poisoning machine learning models to reveal their secrets
Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, and Nicholas Carlini · 2022
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Practical gan-based synthetic ip header trace generation using netshare
Yucheng Yin, Zinan Lin, Minhao Jin, Giulia Fanti, and Vyas Sekar · 2022
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Synthetic text generation with differential privacy: A simple and practical recipe
Xiang Yue, Huseyin A Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Huan Sun, David Levitan, and Robert Sim · 2022
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Privately customizing prefinetuning to better match user data in federated learning
Charlie Hou, Hongyuan Zhan, Akshat Shrivastava, Sid Wang, Aleksandr Livshits, Giulia Fanti, and Daniel Lazar · 2023
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Differentially private synthetic data via foundation model apis 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, and Sergey Yekhanin · 2023
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Dimensionality reduction for general kde mode finding
Xinyu Luo, Christopher Musco, and Cas Widdershoven · 2023
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How to dp-fy ml: A practical guide to machine learning with differential privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Guha Thakurta · 2023
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Fast private kernel density estimation via locality sensitive quantization
Tal Wagner, Yonatan Naamad, and Nina Misrha · 2023
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Selective pre-training for private fine-tuning
Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, and Huishuai Zhang · 2023
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