arXiv Machine Learning June 2, 2026 By Gillian M Raab

It does what it says on the tin: safe synthetic data from coarsened margins

Read the original on arXiv Machine Learning →

arXiv:2606. 02101v1 Announce Type: cross Abstract: This paper proposes a method of creating synthetic data (SD) that will have two important advantages for the user compared to other methods currently available.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 11

Quantifying Membership Disclosure Risk for Tabular Synthetic Data Using Kernel Density Estimators

arXiv:2603. 10937v2 Announce Type: replace Abstract: The use of synthetic data has become increasingly popular as a privacy-preserving alternative to sharing real datasets, especially in sensitive domains such as healthcare, finance, and demography.

By Rajdeep Pathak, Amit Basak, Sayantee Jana

safety

arXiv AI

Jul 13

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy

arXiv:2512. 03238v2 Announce Type: replace-cross Abstract: High quality data is needed to unlock the full potential of AI for end users.

By Natalia Ponomareva, Zheng Xu, H. Brendan McMahan, Peter Kairouz, Lucas Rosenblatt, Vincent Cohen-Addad, Crist\'obal Guzm\'an, Ryan McKenna, Galen Andrew, Alex Bie, Da Yu, Alex Kurakin, Morteza Zadimoghaddam, Sergei Vassilvitskii, Andreas Terzis

benchmarks safety

arXiv Machine Learning

Jun 9

Disjoint Generation of Synthetic Data

arXiv:2507. 19700v2 Announce Type: replace Abstract: We propose a new framework for generating tabular synthetic datasets via disjoint generative models.

By Anton Danholt Lautrup, Muhammad Rajabinasab, Tobias Hyrup, Arthur Zimek, Peter Schneider-Kamp

safety

arXiv Machine Learning

Jun 9

Differentially Private Synthetic Data via APIs 4: Tabular Data

arXiv:2606. 08259v1 Announce Type: new Abstract: This paper investigates the problem of generating synthetic tabular data with differential privacy (DP) guarantees, enabling data sharing in sensitive domains.

By Toan Tran, Arturs Backurs, Zinan Lin, Victor Reis, Li Xiong, Sergey Yekhanin

benchmarks safety

arXiv Machine Learning

Sep 1

Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis

arXiv:2608.29674v1 Announce Type: new Abstract: Sharing tabular data in high-stakes domains is constrained by privacy regulations. Synthetic data offer a promising alternative, but deep generative mo...

By Jinmeng Li, Quan Zhang, Hangting Ye, He Zhao, Firas Laakom, Dandan Guo, J\"urgen Schmidhuber

llms safety

arXiv Machine Learning

Jun 10

LLM-as-a-Discriminator: When Synthetic Tables Still Look Real

arXiv:2606. 09865v1 Announce Type: new Abstract: Privacy and data sharing are often in tension.

By Manel Slokom, Malek Slokom, Thierno Kante

llms safety

One email a morning, machine-written

Related stories

Quantifying Membership Disclosure Risk for Tabular Synthetic Data Using Kernel Density Estimators

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy

Disjoint Generation of Synthetic Data

Differentially Private Synthetic Data via APIs 4: Tabular Data

Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis

LLM-as-a-Discriminator: When Synthetic Tables Still Look Real