Privacy-Preserving Generative Models: A Comprehensive Survey | Paper Citation Record

Observation 2093ac3b-396f-4de8-9fcf-7b15f731d028 · outbound

This paper cites Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang.

Privacy-Preserving Generative Models: A Comprehensive Survey Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang

Reference 1

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Observation 1269de77-02c6-43e6-bf18-3cc4d424f414 · outbound

This paper cites Counterfactual Fairness in Synthetic Data Generation.

Privacy-Preserving Generative Models: A Comprehensive Survey Counterfactual Fairness in Synthetic Data Generation

Reference 2

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Observation 3550bd46-62bb-4f88-8870-5c17799a036b · outbound

This paper cites Differentially Private Mixture of Generative Neural Networks.

Privacy-Preserving Generative Models: A Comprehensive Survey Differentially Private Mixture of Generative Neural Networks

Reference 3

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This paper cites Generalization in Transfer Learning.

Privacy-Preserving Generative Models: A Comprehensive Survey Generalization in Transfer Learning

Reference 4

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Observation cf2e4e7b-5faa-4c2c-ba00-9984c73d4e3e · outbound

This paper cites Differential privacy synthetic data generation using WGANs, 2019.

Privacy-Preserving Generative Models: A Comprehensive Survey Differential privacy synthetic data generation using WGANs, 2019

Reference 5

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This paper cites Wasserstein Generative Adversarial Networks.

Privacy-Preserving Generative Models: A Comprehensive Survey Wasserstein Generative Adversarial Networks

Reference 6

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This paper cites Scott Armstrong and Fred Collopy.

Privacy-Preserving Generative Models: A Comprehensive Survey Scott Armstrong and Fred Collopy

Reference 7

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This paper cites A White-Box Generator Membership Inference Attack Against Generative Models.

Privacy-Preserving Generative Models: A Comprehensive Survey A White-Box Generator Membership Inference Attack Against Generative Models

Reference 8

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This paper cites Differential Privacy Has Disparate Impact on Model Accuracy.

Privacy-Preserving Generative Models: A Comprehensive Survey Differential Privacy Has Disparate Impact on Model Accuracy

Reference 9

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This paper cites Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, Ran Lee, Sanjeev P.

Privacy-Preserving Generative Models: A Comprehensive Survey Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, Ran Lee, Sanjeev P

Reference 10

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This paper cites Privacy and synthetic datasets.

Privacy-Preserving Generative Models: A Comprehensive Survey Privacy and synthetic datasets

Reference 11

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Observation e5de2e55-c8bc-4f3a-81f4-2d5a04f48c62 · outbound

This paper cites Assessing Differentially Private Variational Autoencoders Under Membership Inference.

Privacy-Preserving Generative Models: A Comprehensive Survey Assessing Differentially Private Variational Autoencoders Under Membership Inference

Reference 12

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This paper cites Private GANs, Revisited.

Privacy-Preserving Generative Models: A Comprehensive Survey Private GANs, Revisited

Reference 13

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Observation dbeb20ff-6d71-47aa-8f55-21f18a681447 · outbound

This paper cites SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy.

Privacy-Preserving Generative Models: A Comprehensive Survey SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy

Reference 14

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This paper cites Generative Adversarial Networks: A Survey Toward Private and Secure Applications.

Privacy-Preserving Generative Models: A Comprehensive Survey Generative Adversarial Networks: A Survey Toward Private and Secure Applications

Reference 15

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This paper cites GS-WGAN: a gradient-sanitized approach for learning differentially private generators.

Privacy-Preserving Generative Models: A Comprehensive Survey GS-WGAN: a gradient-sanitized approach for learning differentially private generators

Reference 16

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This paper cites GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models.

Privacy-Preserving Generative Models: A Comprehensive Survey GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models

Reference 17

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Observation 4d61fba2-f260-4f39-83f2-bc4ab1096393 · outbound

This paper cites Differentially Private Generative Adversarial Networks with Model Inversion.

Privacy-Preserving Generative Models: A Comprehensive Survey Differentially Private Generative Adversarial Networks with Model Inversion

Reference 18

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This paper cites Generating a trading strategy in the financial market from sensitive expert data based on the privacy-preserving generative adversarial imitation network.

Privacy-Preserving Generative Models: A Comprehensive Survey Generating a trading strategy in the financial market from sensitive expert data based on the privacy-preserving generative adversarial imitation network

Reference 19

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Observation 6736780c-2fc6-4596-8fc0-c531e5dbfc34 · outbound

This paper cites VGAN-Based Image Representation Learning for Privacy- Preserving Facial Expression Recognition.

Privacy-Preserving Generative Models: A Comprehensive Survey VGAN-Based Image Representation Learning for Privacy- Preserving Facial Expression Recognition

Reference 20

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Observation 25cfce0f-88de-46ec-a1b6-a1a115f547fb · outbound

This paper cites PAR-GAN: Improving the Generalization of Generative Adversarial Networks Against Membership Inference Attacks.

Privacy-Preserving Generative Models: A Comprehensive Survey PAR-GAN: Improving the Generalization of Generative Adversarial Networks Against Membership Inference Attacks

Reference 21

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Privacy-Preserving Generative Models: A Comprehensive Survey Unresolved cited work

Reference 22

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This paper cites Suriyakumar, Natalie Dullerud, Shalmali Joshi, and Marzyeh Ghassemi.

Privacy-Preserving Generative Models: A Comprehensive Survey Suriyakumar, Natalie Dullerud, Shalmali Joshi, and Marzyeh Ghassemi

Reference 23

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Observation e56f35c5-d99c-442a-b09f-d54d6bbdde94 · outbound

This paper cites Generating multi-label discrete patient records using generative adversarial networks.

Privacy-Preserving Generative Models: A Comprehensive Survey Generating multi-label discrete patient records using generative adversarial networks

Reference 24

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This paper cites Croft, Jörg-Rüdiger Sack, and Wei Shi.

Privacy-Preserving Generative Models: A Comprehensive Survey Croft, Jörg-Rüdiger Sack, and Wei Shi

Reference 25

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Observation af3b472f-8838-4364-b16a-3617fed6238b · outbound

This paper cites ArcFace: Additive Angular Margin Loss for Deep Face Recognition.

Privacy-Preserving Generative Models: A Comprehensive Survey ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Reference 26

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Observation 8bf291ef-cbe8-43bb-ae4c-7868053e80ef · outbound

This paper cites A cosine similarity-based negative selection algorithm for time series novelty detection.

Privacy-Preserving Generative Models: A Comprehensive Survey A cosine similarity-based negative selection algorithm for time series novelty detection

Reference 27

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This paper cites Identifying and handling data bias within primary healthcare data using synthetic data generators.

Privacy-Preserving Generative Models: A Comprehensive Survey Identifying and handling data bias within primary healthcare data using synthetic data generators

Reference 28

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Observation 37e89e1d-c0f0-4692-a8f3-2784b3b1d585 · outbound

This paper cites Differential Privacy: A Survey of Results.

Privacy-Preserving Generative Models: A Comprehensive Survey Differential Privacy: A Survey of Results

Reference 29

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This paper cites A survey of differentially private generative adversarial networks.

Privacy-Preserving Generative Models: A Comprehensive Survey A survey of differentially private generative adversarial networks

Reference 30

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Privacy-Preserving Generative Models: A Comprehensive Survey Unresolved cited work

Reference 31

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Observation 3ed5dddb-1f11-4879-b28f-0f3b190c8816 · outbound

This paper cites Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures.

Privacy-Preserving Generative Models: A Comprehensive Survey Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

Reference 32

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This paper cites Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data.

Privacy-Preserving Generative Models: A Comprehensive Survey Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data

Reference 33

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This paper cites Live Face De-Identification in Video.

Privacy-Preserving Generative Models: A Comprehensive Survey Live Face De-Identification in Video

Reference 34

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This paper cites DP-SGD vs PATE: Which Has Less Disparate Impact on GANs?, November 2021.

Privacy-Preserving Generative Models: A Comprehensive Survey DP-SGD vs PATE: Which Has Less Disparate Impact on GANs?, November 2021

Reference 35

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This paper cites Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data.

Privacy-Preserving Generative Models: A Comprehensive Survey Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data

Reference 36

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Privacy-Preserving Generative Models: A Comprehensive Survey Graphical vs

Reference 37

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This paper cites A Unified Framework for Quantifying Privacy Risk in Synthetic Data.

Privacy-Preserving Generative Models: A Comprehensive Survey A Unified Framework for Quantifying Privacy Risk in Synthetic Data

Reference 38

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This paper cites Generation and evaluation of synthetic patient data.

Privacy-Preserving Generative Models: A Comprehensive Survey Generation and evaluation of synthetic patient data

Reference 39

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This paper cites Generative adversarial networks.

Privacy-Preserving Generative Models: A Comprehensive Survey Generative adversarial networks

Reference 40

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This paper cites Improved training of wasserstein gans.

Privacy-Preserving Generative Models: A Comprehensive Survey Improved training of wasserstein gans

Reference 41

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This paper cites Utility-Aware Synthesis of Differentially Private and Attack-Resilient Location Traces.

Privacy-Preserving Generative Models: A Comprehensive Survey Utility-Aware Synthesis of Differentially Private and Attack-Resilient Location Traces

Reference 42

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Observation 68af2541-c019-47bd-a980-1e552d232be3 · outbound

This paper cites Differentially private GANs by adding noise to Discriminator’s loss.

Privacy-Preserving Generative Models: A Comprehensive Survey Differentially private GANs by adding noise to Discriminator’s loss

Reference 43

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This paper cites LOGAN: Membership Inference Attacks Against Generative Models.

Privacy-Preserving Generative Models: A Comprehensive Survey LOGAN: Membership Inference Attacks Against Generative Models

Reference 44

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Observation 53db047a-414c-401e-8d5e-0225335610cb · outbound

This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

Privacy-Preserving Generative Models: A Comprehensive Survey GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 45

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Observation a7dd6db3-b654-4ac2-8a6d-87b69bf7e35d · outbound

This paper cites Monte carlo and reconstruction membership inference attacks against generative models.

Privacy-Preserving Generative Models: A Comprehensive Survey Monte carlo and reconstruction membership inference attacks against generative models

Reference 46

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This paper cites DP-GAN: Differentially private consecutive data publishing using generative adversarial nets.

Privacy-Preserving Generative Models: A Comprehensive Survey DP-GAN: Differentially private consecutive data publishing using generative adversarial nets

Reference 47

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This paper cites Cohen, Owen Daniel, Andrew Elliott, James Geddes, Callum Mole, Camila Rangel-Smith, and Lukasz Szpruch.

Privacy-Preserving Generative Models: A Comprehensive Survey Cohen, Owen Daniel, Andrew Elliott, James Geddes, Callum Mole, Camila Rangel-Smith, and Lukasz Szpruch

Reference 48

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This paper cites TableGAN-MCA: Evaluating Membership Collisions of GAN-Synthesized Tabular Data Releasing.

Privacy-Preserving Generative Models: A Comprehensive Survey TableGAN-MCA: Evaluating Membership Collisions of GAN-Synthesized Tabular Data Releasing

Reference 49

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Observation 430c0bad-6ee7-4512-9edd-6aa04d596211 · outbound

This paper cites Model Extraction and Defenses on Generative Adversarial Networks, January 2021.

Privacy-Preserving Generative Models: A Comprehensive Survey Model Extraction and Defenses on Generative Adversarial Networks, January 2021

Reference 50

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Observation 8dba90c9-faf6-465b-a947-1680adc473a3 · outbound

This paper cites An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models.

Privacy-Preserving Generative Models: A Comprehensive Survey An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models

Reference 51

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Observation 6d786280-33e8-4a26-ac69-2d9b1ac4f85b · outbound

This paper cites Synthetic and Private Smart Health Care Data Generation using GANs.

Privacy-Preserving Generative Models: A Comprehensive Survey Synthetic and Private Smart Health Care Data Generation using GANs

Reference 52

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Observation 8e20d01e-29c1-4c15-a37f-da2297a64fc5 · outbound

This paper cites DP$^2$-V AE: Differentially Private Pre-trained Variational Autoencoders, August 2022.

Privacy-Preserving Generative Models: A Comprehensive Survey DP$^2$-V AE: Differentially Private Pre-trained Variational Autoencoders, August 2022

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.845940Z

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Observation 9461d8f3-900c-4bc5-8fd8-410e46829e9e · outbound

This paper cites Pruning’s Effect on Generalization Through the Lens of Training and Regularization.

Privacy-Preserving Generative Models: A Comprehensive Survey Pruning’s Effect on Generalization Through the Lens of Training and Regularization

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.826494Z

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Observation 05bcb1d2-8316-4915-958c-fa8f34bfe98c · outbound

This paper cites PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees.

Privacy-Preserving Generative Models: A Comprehensive Survey PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.799340Z

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Observation a43e63a6-86af-4e3a-8326-37a5b6fff870 · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Privacy-Preserving Generative Models: A Comprehensive Survey Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 56

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This paper cites A style-based generator architecture for generative adversarial networks.

Privacy-Preserving Generative Models: A Comprehensive Survey A style-based generator architecture for generative adversarial networks

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.778661Z

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Observation a9dae688-ace1-4488-a71a-cc5bafc07c5e · outbound

This paper cites OCT-GAN: Neural ODE-based Conditional Tabular GANs.

Privacy-Preserving Generative Models: A Comprehensive Survey OCT-GAN: Neural ODE-based Conditional Tabular GANs

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.755103Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5de07715-22ce-4270-9307-4774ea7bba7a · outbound

This paper cites Stochastic gradient vb and the variational auto-encoder.

Privacy-Preserving Generative Models: A Comprehensive Survey Stochastic gradient vb and the variational auto-encoder

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.731527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ae29235-e43f-4662-b832-96f8a1eb3651 · outbound

This paper cites PriveTAB: Secure and Privacy- Preserving sharing of Tabular Data.

Privacy-Preserving Generative Models: A Comprehensive Survey PriveTAB: Secure and Privacy- Preserving sharing of Tabular Data

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.707403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bed95510-02ef-4976-bb19-76dde8958f84 · outbound

This paper cites Unnoticeable synthetic face replacement for image privacy protection.

Privacy-Preserving Generative Models: A Comprehensive Survey Unnoticeable synthetic face replacement for image privacy protection

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.688206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 785ca172-1d2c-4682-88d6-8634e76650b2 · outbound

This paper cites DTGAN: Differential Private Training for Tabular GANs, July 2021.

Privacy-Preserving Generative Models: A Comprehensive Survey DTGAN: Differential Private Training for Tabular GANs, July 2021

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.666667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 863aca46-cdae-4947-8346-f86c9542572e · outbound

This paper cites Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis.

Privacy-Preserving Generative Models: A Comprehensive Survey Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.643912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8d74932c-0a0e-45ba-8e43-a42b334e5807 · outbound

This paper cites Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median.

Privacy-Preserving Generative Models: A Comprehensive Survey Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.618535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3e9cb5ad-ce79-4791-af1f-89e637075194 · outbound

This paper cites Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what? PLOS ONE, 12(8):e0183250, August 2017.

Privacy-Preserving Generative Models: A Comprehensive Survey Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what? PLOS ONE, 12(8):e0183250, August 2017

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.597682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dee6b449-09c6-434d-a780-1bd126069650 · outbound

This paper cites Privacy-preserving lightweight face recognition.Neurocomputing, 363(C):212–222, October 2019.

Privacy-Preserving Generative Models: A Comprehensive Survey Privacy-preserving lightweight face recognition.Neurocomputing, 363(C):212–222, October 2019

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.564631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a3735fc8-6242-4250-a860-29aa947fc3c1 · outbound

This paper cites Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images.

Privacy-Preserving Generative Models: A Comprehensive Survey Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.537332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3900782f-0d4f-48ec-8fc8-192fa0aa44a9 · outbound

This paper cites Performing Co-membership Attacks Against Deep Generative Models.

Privacy-Preserving Generative Models: A Comprehensive Survey Performing Co-membership Attacks Against Deep Generative Models

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.515389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f3ca9385-b058-47e0-939f-d56fb08c1618 · outbound

This paper cites Yu, and Yi Wu.

Privacy-Preserving Generative Models: A Comprehensive Survey Yu, and Yi Wu

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.493574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 904fc5f8-2505-4dcd-bf38-ba2a737a7a41 · outbound

This paper cites G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators.

Privacy-Preserving Generative Models: A Comprehensive Survey G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.475586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 01f0008f-6b83-4205-afaa-a389637154ab · outbound

This paper cites POSTER: A Unified Framework of Differentially Private Synthetic Data Release with Generative Adversarial Network.

Privacy-Preserving Generative Models: A Comprehensive Survey POSTER: A Unified Framework of Differentially Private Synthetic Data Release with Generative Adversarial Network

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.455737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e099504f-46a5-4577-a4ca-9ed90b776ddc · outbound

This paper cites Machine Learning for Synthetic Data Generation: A Review.

Privacy-Preserving Generative Models: A Comprehensive Survey Machine Learning for Synthetic Data Generation: A Review

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-09T04:12:46.171445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5df3aafd-f503-4d6a-bac9-d2f530fb64cf · outbound

This paper cites Vincent Poor.

Privacy-Preserving Generative Models: A Comprehensive Survey Vincent Poor

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.426521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bdce4ad5-8f59-4f84-b1fb-8f1446b59a4a · outbound

This paper cites CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks.

Privacy-Preserving Generative Models: A Comprehensive Survey CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.400768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6a4e63c4-6c2f-4282-a39a-4fdcd6b6c84e · outbound

This paper cites Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy.

Privacy-Preserving Generative Models: A Comprehensive Survey Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.383873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ed4a9321-6ca4-4ffb-a112-8a535fc9175c · outbound

This paper cites A Probe Towards Understanding GAN and V AE Models, December.

Privacy-Preserving Generative Models: A Comprehensive Survey A Probe Towards Understanding GAN and V AE Models, December

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.367196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c037f2fa-6b77-4bc6-b7b7-0329c9dbaecb · outbound

This paper cites Rényi Differential Privacy.

Privacy-Preserving Generative Models: A Comprehensive Survey Rényi Differential Privacy

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.349075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f68af20f-66ab-4b86-b767-e31507c8684d · outbound

This paper cites Conditional Generative Adversarial Nets.

Privacy-Preserving Generative Models: A Comprehensive Survey Conditional Generative Adversarial Nets

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-09T04:12:46.208134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 192dd9f7-57b8-4ff2-b6f1-e2899cdbe936 · outbound

This paper cites Spectral Normalization for Generative Adversarial Networks.

Privacy-Preserving Generative Models: A Comprehensive Survey Spectral Normalization for Generative Adversarial Networks

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.332582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ed92e88c-2670-4c68-8a24-4943a2917508 · outbound

This paper cites an unresolved cited work.

Privacy-Preserving Generative Models: A Comprehensive Survey Unresolved cited work

Reference 80

Resolution: unresolved; raw_fallback, observed 2026-08-09T04:12:47.313755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1090a130-d3c5-4291-b9ee-a6a6e227756b · outbound

This paper cites DPD-InfoGAN: Differentially Private Distributed InfoGAN.

Privacy-Preserving Generative Models: A Comprehensive Survey DPD-InfoGAN: Differentially Private Distributed InfoGAN

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.294966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e0d74ef7-6a89-4417-bbf2-147132e90dbf · outbound

This paper cites an unresolved cited work.

Privacy-Preserving Generative Models: A Comprehensive Survey Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-09T04:12:47.277432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 24e1758d-d1f1-436e-afdf-7500b06f9650 · outbound

This paper cites Automatic detection of outliers and the number of clusters in k-means clustering via Chebyshev-type inequalities.

Privacy-Preserving Generative Models: A Comprehensive Survey Automatic detection of outliers and the number of clusters in k-means clustering via Chebyshev-type inequalities

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.259811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e6b904dc-a64c-400d-8e53-defb9f966a81 · outbound

This paper cites On Utility and Privacy in Synthetic Genomic Data.

Privacy-Preserving Generative Models: A Comprehensive Survey On Utility and Privacy in Synthetic Genomic Data

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.239184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f76f07f6-c9a3-44bf-a4da-7adcc592b721 · outbound

This paper cites Privacy-enhanced generative adversarial network with adaptive noise allocation.

Privacy-Preserving Generative Models: A Comprehensive Survey Privacy-enhanced generative adversarial network with adaptive noise allocation

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.219540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c15464a5-ffd3-4266-afa4-b4152a7943ac · outbound

This paper cites Scalable Private Learning with PATE.

Privacy-Preserving Generative Models: A Comprehensive Survey Scalable Private Learning with PATE

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.200159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2b0ebfb0-d403-41fb-bd9a-c6c3d005920d · outbound

This paper cites Evaluating Differentially Private Generative Adversarial Networks Over Membership Inference Attack.

Privacy-Preserving Generative Models: A Comprehensive Survey Evaluating Differentially Private Generative Adversarial Networks Over Membership Inference Attack

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.182091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9d6c98ca-e55c-47e3-a806-f72164f117cb · outbound

This paper cites Data synthesis based on generative adversarial networks.

Privacy-Preserving Generative Models: A Comprehensive Survey Data synthesis based on generative adversarial networks

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.164122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cf7d2506-91fb-41db-b11f-e600f10cda0e · outbound

This paper cites Unsupervised representation learning with deep convolutional generative adversarial networks, 2016.

Privacy-Preserving Generative Models: A Comprehensive Survey Unsupervised representation learning with deep convolutional generative adversarial networks, 2016

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-09T04:12:46.272489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdc5cd49-7951-4f28-8e03-baf0d1e1a4e1 · outbound

This paper cites Improved Techniques for Training GANs.

Privacy-Preserving Generative Models: A Comprehensive Survey Improved Techniques for Training GANs

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.134356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fd3292e4-9025-489e-a502-c6cd518f71db · outbound

This paper cites Differentially-Private Text Generation via Text Preprocessing to Reduce Utility Loss.

Privacy-Preserving Generative Models: A Comprehensive Survey Differentially-Private Text Generation via Text Preprocessing to Reduce Utility Loss

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.117977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fe32b96d-0450-4d4d-b3e8-b67d7df44c53 · outbound

This paper cites FaceNet: A unified embedding for face recognition and clustering.

Privacy-Preserving Generative Models: A Comprehensive Survey FaceNet: A unified embedding for face recognition and clustering

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.099682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 741cc6a8-92d2-43a2-b242-6f34c8a7902f · outbound

This paper cites Membership inference attacks against machine learning models.

Privacy-Preserving Generative Models: A Comprehensive Survey Membership inference attacks against machine learning models

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.083591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3afdc648-d930-45f0-b9df-a9996d2faa4f · outbound

This paper cites Synthetic Data – Anonymisation Groundhog Day.

Privacy-Preserving Generative Models: A Comprehensive Survey Synthetic Data – Anonymisation Groundhog Day

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.066627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c07a2c61-8fc1-44bf-87f0-327e614d1df6 · outbound

This paper cites Adversarial Attacks Against Deep Generative Models on Data: A Survey.

Privacy-Preserving Generative Models: A Comprehensive Survey Adversarial Attacks Against Deep Generative Models on Data: A Survey

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.050839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ac5f680e-9404-41a1-9489-581ba229c88b · outbound

This paper cites P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model.

Privacy-Preserving Generative Models: A Comprehensive Survey P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.033351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d744297e-a9a1-42b4-9d3e-ba700fabfb8a · outbound

This paper cites Differ- entially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning.

Privacy-Preserving Generative Models: A Comprehensive Survey Differ- entially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:47.015452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e41abc14-e553-4641-b48f-5bbe48156ae6 · outbound

This paper cites Fairness and privacy preservation for facial images: GAN-based methods.

Privacy-Preserving Generative Models: A Comprehensive Survey Fairness and privacy preservation for facial images: GAN-based methods

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:46.998569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5187e263-704f-4fe2-9064-4bfcc491eaba · outbound

This paper cites Fox, and Chandan K.

Privacy-Preserving Generative Models: A Comprehensive Survey Fox, and Chandan K

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:46.982182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5fb31851-163f-480e-8a8c-61f364b9c261 · outbound

This paper cites DP-CGAN: Differentially Private Synthetic Data and Label Generation.

Privacy-Preserving Generative Models: A Comprehensive Survey DP-CGAN: Differentially Private Synthetic Data and Label Generation

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:12:46.965475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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