Fetching the paper…
Reading the bibliography…
We propose a scheme for auditing differentially private machine learning systems with a single training run.
“Collusion-secure fingerprinting for digital data”
Dan Boneh and James Shaw · 1905
Earlier work this paper cites.
“Computing tight differential privacy guarantees using fft”
Antti Koskela, Joonas J“”alk“”o and Antti Honkela · 1906
Earlier work this paper cites.
“R \ \backslash ’enyi differential privacy of the sampled gaussian mechanism”
Ilya Mironov, Kunal Talwar and Li Zhang · 1908
Earlier work this paper cites.
“A new analysis of differential privacy’s generalization guarantees”
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Moshe Shenfeld · 1909
Earlier work this paper cites.
“Membership inference attacks from first principles”
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis and Florian Tramer · 1914
Earlier work this paper cites.
“Reasoning about generalization via conditional mutual information”
Thomas Steinke and Lydia Zakynthinou · 2001
Earlier work this paper cites.
“Our data, ourselves: Privacy via distributed noise generation”
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov and Moni Naor · 2006
Earlier work this paper cites.
“Calibrating noise to sensitivity in private data analysis”
Cynthia Dwork, Frank McSherry, Kobbi Nissim and Adam Smith · 2006
Earlier work this paper cites.
“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 Pearson, Dietrich Stephan, Stanley Nelson and David Craig · 2008
Earlier work this paper cites.
“Optimal probabilistic fingerprint codes”
G“’abor Tardos · 2008
Earlier work this paper cites.
“Genomic privacy and limits of individual detection in a pool”
Sriram Sankararaman, Guillaume Obozinski, Michael Jordan and Eran Halperin · 2009
Earlier work this paper cites.
“Private empirical risk minimization: Efficient algorithms and tight error bounds”
Raef Bassily, Adam Smith and Abhradeep Thakurta · 2014
Earlier work this paper cites.
“The algorithmic foundations of differential privacy”
Cynthia Dwork and Aaron Roth · 2014
Earlier work this paper cites.
“On the’semantics’ of differential privacy: A bayesian formulation”
Shiva Kasiviswanathan and Adam Smith · 2014
Earlier work this paper cites.
“Generalization in adaptive data analysis and holdout reuse”
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold and Aaron Roth · 2015
Earlier work this paper cites.
“Preserving statistical validity in adaptive data analysis”
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold and Aaron Roth · 2015
Earlier work this paper cites.
“Robust traceability from trace amounts”
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman and Salil Vadhan · 2015
Earlier work this paper cites.
“The composition theorem for differential privacy”
Peter Kairouz, Sewoong Oh and Pramod Viswanath · 2015
Cited alongside, same era.
“The complexity of computing the optimal composition of differential privacy”
Jack Murtagh and Salil Vadhan · 2015
Cited alongside, same era.
“Deep learning with differential privacy”
Martin Abadi, Andy Chu, Ian Goodfellow, H McMahan, Ilya Mironov, Kunal Talwar and Li Zhang · 2016
Cited alongside, same era.
“Algorithmic stability for adaptive data analysis”
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer and Jonathan Ullman · 2016
Cited alongside, same era.
“Concentrated differential privacy: Simplifications, extensions, and lower bounds”
“Evaluating differentially private machine learning in practice”
Bargav Jayaraman and David Evans · 2019
Later among the works it cites.
“Subsampled rényi differential privacy and analytical moments accountant”
Yu-Xiang Wang, Borja Balle and Shiva Kasiviswanathan · 2019
Later among the works it cites.
“Auditing differentially private machine learning: How private is private sgd?”
Matthew Jagielski, Jonathan Ullman and Alina Oprea · 2020
Later among the works it cites.
“Numerical composition of differential privacy”
Sivakanth Gopi, Yin Lee and Lukas Wutschitz · 2021
Later among the works it cites.
“Antipodes of label differential privacy: Pate and alibi”
Mani Malek, Ilya Mironov, Karthik Prasad, Igor Shilov and Florian Tramer · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
“Concentrated differential privacy”
Cynthia Dwork and Guy Rothblum · 2016
Cited alongside, same era.
“Max-information, differential privacy, and post-selection hypothesis testing”
Ryan Rogers, Aaron Roth, Adam Smith and Om Thakkar · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
“Generalization for adaptively-chosen estimators via stable median”
Vitaly Feldman and Thomas Steinke · 2017
Cited alongside, same era.
Ilya Mironov · 2017
Cited alongside, same era.
“Membership inference attacks against machine learning models”
Reza Shokri, Marco Stronati, Congzheng Song and Vitaly Shmatikov · 2017
Cited alongside, same era.
“The complexity of differential privacy”
Salil Vadhan · 2017
Cited alongside, same era.
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot and Nicholas Carlini · 2021
Later among the works it cites.
“Unlocking high-accuracy differentially private image classification through scale”
Soham De, Leonard Berrada, Jamie Hayes, Samuel Smith and Borja Balle · 2022
Later among the works it cites.
“A General Framework for Auditing Differentially Private Machine Learning”
Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro and Brian Testa · 2022
Later among the works it cites.
“CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning”
Samuel Maddock, Alexandre Sablayrolles and Pierre Stock · 2022
Later among the works it cites.
“Composition of Differential Privacy & Privacy Amplification by Subsampling”
Thomas Steinke · 2022
Later among the works it cites.
“Debugging differential privacy: A case study for privacy auditing”
Florian Tramer, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski and Nicholas Carlini · 2022
Later among the works it cites.
“Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries”
Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping and Tom Goldstein · 2022
Later among the works it cites.
“Bayesian estimation of differential privacy”
Santiago Zanella-B“’eguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor R“”uhle, Andrew Paverd, Mohammad Naseri and Boris K“”opf · 2022
Later among the works it cites.
“Optimal accounting of differential privacy via characteristic function”
Yuqing Zhu, Jinshuo Dong and Yu-Xiang Wang · 2022
Later among the works it cites.
“One-shot Empirical Privacy Estimation for Federated Learning”
Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H McMahan and Vinith Suriyakumar · 2023
Closest in time.
“Tight Auditing of Differentially Private Machine Learning”
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tram“‘er, Matthew Jagielski, Nicholas Carlini and Andreas Terzis · 2023
Closest in time.