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Differentially private synthetic data generation (DP-SDG) algorithms are used to release datasets that are structurally and statistically similar to sensitive data while providing formal bounds on the information they leak.
The use of confidence or fiducial limits illustrated in the case of the binomial
Charles J Clopper and Egon S Pearson · 1934
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UCI ADULT Data Set
Ronny Kohavi and Barry Becker · 1996
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k-anonymity: A model for protecting privacy
Latanya Sweeney · 2002
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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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l-diversity: Privacy beyond k-anonymity
Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, and Muthuramakrishnan Venkitasubramaniam · 2007
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RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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The Composition Theorem for Differential Privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 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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Fire Department Calls for Service
DataSF · 2016
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The Synthetic data vault
Neha Patki, Roy Wedge, and Kalyan Veeramachaneni · 2016
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Learning with Privacy at Scale
Apple · 2017
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DataSynthesizer: Privacy-Preserving Synthetic Datasets
Haoyue Ping, Julia Stoyanovich, and Bill Howe · 2017
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Membership Inference Attacks against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Privacy Loss in Apple’s Implementation of Differential Privacy on MacOS 10.12
Jun Tang, Aleksandra Korolova, Xiaolong Bai, Xueqiang Wang, and Xiaofeng Wang · 2017
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PrivBayes: Private Data Release via Bayesian Networks
Jun Zhang, Graham Cormode, Cecilia M Procopiuc, Divesh Srivastava, and Xiaokui Xiao · 2017
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PATE-GAN: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela Van Der Schaar · 2018
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Comprehensive privacy analysis of deep learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
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2018 Differential Privacy Synthetic Data Challenge
National Institute of Standards and Technology · 2018
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Knock Knock, Who’s There? Membership Inference on Aggregate Location Data
Apostolos Pyrgelis, Carmela Troncoso, and Emiliano De Cristofaro · 2018
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Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
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Differentially Private Dataset Release using Wasserstein GANs
Moustafa Alzantot and Mani Srivastava · 2019
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Jinshuo Dong, Aaron Roth, and Weijie J Su · 2019
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TensorFlow Privacy
Google · 2019
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LOGAN: Membership Inference Attacks Against Generative Models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2019
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Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models
Benjamin Hilprecht, Martin Härterich, and Daniel Bernau · 2019
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Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 2019
Cited alongside, same era.
Empirical Evaluation on Synthetic Data Generation with Generative Adversarial Network
Pei-Hsuan Lu, Pang-Chieh Wang, and Chia-Mu Yu · 2019
Cited alongside, same era.
Assessing privacy and quality of synthetic health data
Andrew Yale, Saloni Dash, Ritik Dutta, Isabelle Guyon, Adrien Pavao, and Kristin P Bennett · 2019
Cited alongside, same era.
Auditing Differentially Private Machine Learning: How Private is Private SGD?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea · 2020
Cited alongside, same era.
GitHub Pull Request: fix prng key reuse in differential privacy example
Matthew Johnson · 2020
Cited alongside, same era.
Differentially Private Query Release Through Adaptive Projection
Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, and Ankit A Siva · 2021
IOM and Microsoft release first-ever differentially private synthetic dataset to counter human trafficking
Microsoft · 2022
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DP-Opt: Identify High Differential Privacy Violation by Optimization
Ben Niu, Zejun Zhou, Yahong Chen, Jin Cao, and Fenghua Li · 2022
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Synthetic Data – Anonymisation Groundhog Day
Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso · 2022
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$1.2 million to study synthetic data use
UC Davis · 2022
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Enhanced membership inference attacks against machine learning models
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, Vincent Bindschaedler, and Reza Shokri · 2022
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Read the QA Report
Mostly AI · 2023
Later among the works it cites.
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Cited alongside, same era.
DP-Sniper: Black-Box Discovery of Differential Privacy Violations using Classifiers
Benjamin Bichsel, Samuel Steffen, Ilija Bogunovic, and Martin Vechev · 2021
Cited alongside, same era.
The Census Bureau’s Simulated Reconstruction-Abetted Re-identification Attack on the 2010 Census
US Census Bureau · 2021
Cited alongside, same era.
Data Synthesis via Differentially Private Markov Random Fields
Kuntai Cai, Xiaoyu Lei, Jianxin Wei, and Xiaokui Xiao · 2021
Cited alongside, same era.
Label-Only Membership Inference Attacks
Christopher A Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot · 2021
Cited alongside, same era.
DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges
Ninghui Li, Zhikun Zhang, and Tianhao Wang · 2021
Cited alongside, same era.
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods
Terrance Liu, Giuseppe Vietri, and Steven Z Wu · 2021
Cited alongside, same era.
A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data
Meenatchi Sundaram Muthu Selva Annamalai, Andrea Gadotti, and Luc Rocher · 2023
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Privacy Side Channels in Machine Learning Systems
Edoardo Debenedetti, Giorgio Severi, Nicholas Carlini, Christopher A Choquette-Choo, Matthew Jagielski, Milad Nasr, Eric Wallace, and Florian Tramèr · 2023
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A list of real-world uses of differential privacy
Damien Desfontaines · 2023
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Synthesising the linked 2011 Census and deaths dataset while preserving its confidentiality
Office for National Statistics · 2023
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Understanding how Differentially Private Generative Models Spend their Privacy Budget
Georgi Ganev, Kai Xu, and Emiliano De Cristofaro · 2023
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Florent Guépin, Matthieu Meeus, Ana-Maria Cretu, and Yves-Alexandre de Montjoye · 2023
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Group and Attack: Auditing Differential Privacy
Johan Lokna, Anouk Paradis, Dimitar I Dimitrov, and Martin Vechev · 2023
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CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning
Samuel Maddock, Alexandre Sablayrolles, and Pierre Stock · 2023
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Achilles’ Heels: Vulnerable Record Identification in Synthetic Data Publishing
Matthieu Meeus, Florent Guepin, Ana-Maria Cretu, and Yves-Alexandre de Montjoye · 2023
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Tight Auditing of Differentially Private Machine Learning
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, and Andreas Terzis · 2023
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Synthcity: facilitating innovative use cases of synthetic data in different data modalities
Zhaozhi Qian, Bogdan-Constantin Cebere, and Mihaela van der Schaar · 2023
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Statice by Anonos
Statice · 2023
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Privacy Auditing with One (1) Training Run
Thomas Steinke, Milad Nasr, and Matthew Jagielski · 2023
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Syntegra
Syntegra · 2023
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DPMLBench: Holistic Evaluation of Differentially Private Machine Learning
Chengkun Wei, Minghu Zhao, Zhikun Zhang, Min Chen, Wenlong Meng, Bo Liu, Yuan Fan, and Wenzhi Chen · 2023
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Bayesian estimation of differential privacy
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Rühle, Andrew Paverd, Mohammad Naseri, Boris Köpf, and Daniel Jones · 2023
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One-shot Empirical Privacy Estimation for Federated Learning
Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H Brendan McMahan, and Vinith Suriyakumar · 2024
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Differential Privacy Synthetic Data Challenge Algorithms
National Institute of Standards and Technology · 2024
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