Cover Image

Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Maddock, Samuel, Gade, Shripad, Cormode, Graham, Bullock, Will
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Synthetic Tabular Data: Methods, Attacks and Defenses
by: Cormode, Graham, et al.
Published: (2025)

GEM+: Scalable State-of-the-Art Private Synthetic Data with Generator Networks
by: Maddock, Samuel, et al.
Published: (2025)

FLAIM: AIM-based Synthetic Data Generation in the Federated Setting
by: Maddock, Samuel, et al.
Published: (2023)

Federated Computation of ROC and PR Curves
by: Xu, Xuefeng, et al.
Published: (2025)

Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?
by: Swanberg, Marika, et al.
Published: (2025)

Differentially Private Synthetic Data Generation for Relational Databases
by: Alimohammadi, Kaveh, et al.
Published: (2024)

Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints
by: Shejwalkar, Virat, et al.
Published: (2022)

SpinML: Customized Synthetic Data Generation for Private Training of Specialized ML Models
by: Zhang, Jiang, et al.
Published: (2025)

Private Federated Multiclass Post-hoc Calibration
by: Maddock, Samuel, et al.
Published: (2025)

VFLGAN-TS: Vertical Federated Learning-based Generative Adversarial Networks for Publication of Vertically Partitioned Time-Series Data
by: Yuan, Xun, et al.
Published: (2024)

Oracle-Efficient Differentially Private Learning with Public Data
by: Block, Adam, et al.
Published: (2024)

Optimal Locally Private Nonparametric Classification with Public Data
by: Ma, Yuheng, et al.
Published: (2023)

Pre-training Differentially Private Models with Limited Public Data
by: Bu, Zhiqi, et al.
Published: (2024)

Differentially Private Synthetic Data Generation Using Context-Aware GANs
by: Kotal, Anantaa, et al.
Published: (2025)

EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors
by: Banayeeanzade, Amin, et al.
Published: (2026)

The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data
by: Ganev, Georgi, et al.
Published: (2025)

Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
by: Zhou, Yvonne, et al.
Published: (2024)

SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy
by: Nahid, Md Mahadi Hasan, et al.
Published: (2024)

Private Training & Data Generation by Clustering Embeddings
by: Zhou, Felix, et al.
Published: (2025)

VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication
by: Yuan, Xun, et al.
Published: (2024)

Locally Private Estimation with Public Features
by: Ma, Yuheng, et al.
Published: (2024)

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
by: Zhou, Yan, et al.
Published: (2025)

dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
by: Mahiou, Sofiane, et al.
Published: (2025)

ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?
by: Liu, Peihan, et al.
Published: (2026)

End to End Collaborative Synthetic Data Generation
by: Pentyala, Sikha, et al.
Published: (2024)

Towards Biologically Plausible and Private Gene Expression Data Generation
by: Chen, Dingfan, et al.
Published: (2024)

Differentially Private Synthetic Data Release for Topics API Outputs
by: Dick, Travis, et al.
Published: (2025)

PrivORL: Differentially Private Synthetic Dataset for Offline Reinforcement Learning
by: Gong, Chen, et al.
Published: (2025)

Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)
by: Mori, Junki, et al.
Published: (2025)

Efficient Public Verification of Private ML via Regularization
by: Bell, Zoë Ruha, et al.
Published: (2025)

Differentially Private Knowledge Distillation via Synthetic Text Generation
by: Flemings, James, et al.
Published: (2024)

Verifiable Differential Privacy
by: Biswas, Ari, et al.
Published: (2022)

Optimal Differentially Private Model Training with Public Data
by: Lowy, Andrew, et al.
Published: (2023)

Lower Bounds for Public-Private Learning under Distribution Shift
by: Setlur, Amrith, et al.
Published: (2025)

Scalable and Privacy-Preserving Synthetic Data Generation on Decentralised Web
by: Ramesh, Vishal, et al.
Published: (2023)

On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift
by: Thaker, Pratiksha, et al.
Published: (2023)

Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
by: Tramèr, Florian, et al.
Published: (2022)

LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples
by: Wu, Huiyu, et al.
Published: (2024)

Improving the Utility of Differentially Private Clustering through Dynamical Processing
by: Byun, Junyoung, et al.
Published: (2023)

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
by: Inane, Ahmed Mehdi, et al.
Published: (2026)