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Generative models producing synthetic data are meant to provide a privacy-friendly approach to releasing data.
Revealing information while preserving privacy
I. Dinur and K. Nissim · 2003
Earlier work this paper cites.
Our data, ourselves: Privacy via distributed noise generation
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor · 2006
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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The price of privacy and the limits of LP decoding
C. Dwork, F. McSherry, and K. Talwar · 2007
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New efficient attacks on statistical disclosure control mechanisms
C. Dwork and S. Yekhanin · 2008
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MNIST handwritten digit database
Y. LeCun, C. Cortes, and C. Burges · 2010
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Opinion on anonymisation techniques
A29WP · 2014
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The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
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Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart · 2014
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Differential privacy: an economic method for choosing epsilon
J. Hsu, M. Gaboardi, A. Haeberlen, S. Khanna, A. Narayan, B. C. Pierce, and A. Roth · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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UCI machine learning repository
D. Dua and C. Graff · 2017
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Semi-supervised knowledge transfer for deep learning from private training data
N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, and K. Talwar · 2017
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DataSynthesizer: Privacy-preserving synthetic datasets
H. Ping, J. Stoyanovich, and B. Howe · 2017
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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Machine learning models that remember too much
C. Song, T. Ristenpart, and V. Shmatikov · 2017
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PrivBayes: private data release via bayesian networks
J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao · 2017
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The creation and use of the SIPP synthetic Beta v7. 0
G. Benedetto, J. C. Stanley, E. Totty, et al · 2018
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Preliminary opinion on privacy by design
EDPS · 2018
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PATE-GAN: generating synthetic data with differential privacy guarantees
J. Jordon, J. Yoon, and M. Van Der Schaar · 2018
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2018 Differential privacy synthetic data challenge
NIST · 2018
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Privacy and data confidentiality methods: a data and analysis method review
ONS · 2018
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SoK: Security and privacy in machine learning
N. Papernot, P. McDaniel, A. Sinha, and M. P. Wellman · 2018
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Scalable private learning with pate
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and Ú. Erlingsson · 2018
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Data synthesis based on generative adversarial networks
N. Park, M. Mohammadi, K. Gorde, S. Jajodia, H. Park, and Y. Kim · 2018
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On Location, Time, and Membership: Studying How Aggregate Location Data Can Harm Users’ Privacy
A. Pyrgelis · 2018
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Differentially private generative adversarial network
L. Xie, K. Lin, S. Wang, F. Wang, and J. Zhou · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
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Differential privacy has disparate impact on model accuracy
E. Bagdasaryan, O. Poursaeed, and V. Shmatikov · 2019
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Distribution density, tails, and outliers in machine learning: Metrics and applications
N. Carlini, U. Erlingsson, and N. Papernot · 2019
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The secret sharer: Evaluating and testing unintended memorization in neural networks
N. Carlini, C. Liu, Ú. Erlingsson, J. Kos, and D. Song · 2019
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When the signal is in the noise: Exploiting Diffix’s Sticky Noise
A. Gadotti, F. Houssiau, L. Rocher, B. Livshits, and Y.-A. De Montjoye · 2019
Earlier work this paper cites.
Understanding database reconstruction attacks on public data
S. Garfinkel, J. M. Abowd, and C. Martindale · 2019
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LOGAN: membership inference attacks against generative models
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro · 2019
Earlier work this paper cites.
Monte Carlo and reconstruction membership inference attacks against generative models
B. Hilprecht, M. Härterich, and D. Bernau · 2019
Earlier work this paper cites.
Empirical evaluation on synthetic data generation with generative adversarial network
P.-H. Lu, P.-C. Wang, and C.-M. Yu · 2019
Earlier work this paper cites.
Graphical-model based estimation and inference for differential privacy
R. McKenna, D. Sheldon, and G. Miklau · 2019
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Beyond inferring class representatives: User-level privacy leakage from federated learning
Z. Wang, M. Song, Z. Zhang, Y. Song, Q. Wang, and H. Qi · 2019
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Detecting overfitting of deep generative networks via latent recovery
R. Webster, J. Rabin, L. Simon, and F. Jurie · 2019
Earlier work this paper cites.
Modeling tabular data using conditional gan
L. Xu, M. Skoularidou, A. Cuesta-Infante, and K. Veeramachaneni · 2019
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Deep leakage from gradients
L. Zhu, Z. Liu, and S. Han · 2019
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Security engineering: a guide to building dependable distributed systems
R. Anderson · 2020
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GAN-Leaks: A taxonomy of membership inference attacks against generative models
D. Chen, N. Yu, Y. Zhang, and M. Fritz · 2020
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Linear program reconstruction in practice
A. Cohen and K. Nissim · 2020
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Towards formalizing the GDPR’s notion of singling out
A. Cohen and K. Nissim · 2020
Earlier work this paper cites.
Inverting gradients-how easy is it to break privacy in federated learning?
J. Geiping, H. Bauermeister, H. Dröge, and M. Moeller · 2020
Earlier work this paper cites.
Auditing differentially private machine learning: How private is private sgd?
M. Jagielski, J. Ullman, and A. Oprea · 2020
Earlier work this paper cites.
Truly anonymous synthetic data – evolving legal definitions and technologies (part II)
Mostly AI · 2020
Cited alongside, same era.
2020 Differential privacy temporal map challenge
NIST · 2020
Cited alongside, same era.
Practical synthetic data generation: balancing privacy and the broad availability of data
Replica Analytics · 2020
Cited alongside, same era.
Updates-leak: Data set inference and reconstruction attacks in online learning
A. M. G. Salem, A. Bhattacharyya, M. Backes, M. Fritz, and Y. Zhang · 2020
Cited alongside, same era.
Beyond privacy trade-offs with structured transparency
A. Trask, E. Bluemke, B. Garfinkel, C. G. Cuervas-Mons, and A. Dafoe · 2020
Cited alongside, same era.
Analyzing information leakage of updates to natural language models
Patient-centric synthetic data generation, no reason to risk re-identification in biomedical data analysis
M. Guillaudeux, O. Rousseau, J. Petot, et al · 2023
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Analyzing privacy leakage in machine learning via multiple hypothesis testing: A lesson from Fano
C. Guo, A. Sablayrolles, and M. Sanjabi · 2023
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TabDDPM: Modelling tabular data with diffusion models
A. Kotelnikov, D. Baranchuk, I. Rubachev, and A. Babenko · 2023
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Arbitrary decisions are a hidden cost of differentially-private training
B. Kulynych, H. Hsu, C. Troncoso, and F. P. Calmon · 2023
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Tabular data synthesis with generative adversarial networks: design space and optimizations
T. Liu, J. Fan, G. Li, N. Tang, and X. Du · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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S. Zanella-Béguelin, L. Wutschitz, S. Tople, V. Rühle, A. Paverd, O. Ohrimenko, B. Köpf, and M. Brockschmidt · 2020
Cited alongside, same era.
Extracting training data from large language models
N. Carlini, F. Tramer, E. Wallace, et al · 2021
Cited alongside, same era.
“I need a better description”: An investigation into user expectations for differential privacy
R. Cummings, G. Kaptchuk, and E. M. Redmiles · 2021
Cited alongside, same era.
Introducing Gretel’s privacy filters
Gretel · 2021
Cited alongside, same era.
Winning the NIST Contest: a scalable and general approach to differentially private synthetic data
R. McKenna, G. Miklau, and D. Sheldon · 2021
Cited alongside, same era.
Adversary instantiation: Lower bounds for differentially private machine learning
M. Nasr, S. Songi, A. Thakurta, N. Papernot, and N. Carlin · 2021
Cited alongside, same era.
A&E synthetic data
NHS England · 2021
Cited alongside, same era.
M. Meeus, F. Guepin, A.-M. Cretu, and Y.-A. de Montjoye · 2023
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What is data augmentation and how to use it to supercharge your data?
Mostly AI · 2023
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Scalable extraction of training data from (production) language models
M. Nasr, N. Carlini, J. Hayase, M. Jagielski, A. F. Cooper, D. Ippolito, C. A. Choquette-Choo, E. Wallace, F. Tramèr, and K. Lee · 2023
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Tight auditing of differentially private machine learning
M. Nasr, J. Hayes, T. Steinke, B. Balle, F. Tramèr, M. Jagielski, N. Carlini, and A. Terzis · 2023
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Synthesising the linked 2011 Census and deaths dataset while preserving its confidentiality
ONS · 2023
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A deep learning-based pipeline for the generation of synthetic tabular data
D. Panfilo, A. Boudewijn, S. Saccani, A. Coser, B. Svara, C. Chauvenet, C. Mami, and E. Medvet · 2023
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Diverse community data for benchmarking data privacy algorithms
A. Sen, C. Task, D. Kapur, G. S. Howarth, and K. Bhagat · 2023
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GenerativeMTD: A deep synthetic data generation framework for small datasets
J. Sivakumar, K. Ramamurthy, M. Radhakrishnan, and D. Won · 2023
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REaLTabFormer: Generating realistic relational and tabular data using transformers
A. V. Solatorio and O. Dupriez · 2023
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Can secure synthetic data maintain data utility?
Tonic · 2023
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Membership inference attacks against synthetic data through overfitting detection
B. van Breugel, H. Sun, Z. Qian, and M. van der Schaar · 2023
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How to evaluate the re-identification risk in Synthetic Data?
YData · 2023
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EHR-Safe: Generating high-fidelity and privacy-preserving synthetic electronic health records
J. Yoon, M. Mizrahi, N. F. Ghalaty, et al · 2023
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Generative table pre-training empowers models for tabular prediction
T. Zhang, S. Wang, S. Yan, J. Li, and Q. Liu · 2023
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Data forensics in diffusion models: A systematic analysis of membership privacy
D. Zhu, D. Chen, J. Grossklags, and M. Fritz · 2023
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https://www.aindo.com/ , 2024
Aindo · 2024
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A linear reconstruction approach for attribute inference attacks against synthetic data
M. S. M. S. Annamalai, A. Gadotti, and L. Rocher · 2024
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Stealing part of a production language model
N. Carlini, D. Paleka, K. D. Dvijotham, T. Steinke, J. Hayase, A. F. Cooper, K. Lee, M. Jagielski, M. Nasr, A. Conmy, et al · 2024
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https://datacebo.com/ , 2024
DataCebo · 2024
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Synthetic data metrics
DataCebo · 2024
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Privacy side channels in machine learning systems
E. Debenedetti, G. Severi, N. Carlini, C. A. Choquette-Choo, M. Jagielski, M. Nasr, E. Wallace, and F. Tramèr · 2024
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Graphical vs. deep generative models: Measuring the impact of differentially private mechanisms and budgets on utility
G. Ganev, K. Xu, and E. De Cristofaro · 2024
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https://gretel.ai/ , 2024
Gretel · 2024
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Conditional generation faq
Gretel · 2024
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Deployment options
Gretel · 2024
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Synthetic is all you need: Removing the auxiliary data assumption for membership inference attacks against synthetic data
F. Guépin, M. Meeus, A.-M. Cretu, and Y.-A. de Montjoye · 2024
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Bounding training data reconstruction in DP-SGD
J. Hayes, B. Balle, and S. Mahloujifar · 2024
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https://hazy.com/ , 2024
Hazy · 2024
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Architecture
Hazy · 2024
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https://mostly.ai/ , 2024
Mostly AI · 2024
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Deploy Mostly AI
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Statice · 2024
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Syntegra · 2024
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https://www.synthesized.io/ , 2024
Synthesized · 2024
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Strict synthesis
Synthesized · 2024
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https://www.syntho.ai/ , 2024
Syntho · 2024
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Tonic · 2024
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Deploying a self-hosted Structural instance
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YData · 2024
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Mixed-Type tabular data synthesis with score-based diffusion in latent space
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