arXiv Machine Learning August 11, 2026 By Rajdeep Pathak, Amit Basak, Sayantee Jana

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

safety

arXiv Machine Learning

Jul 16

When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training

arXiv:2607. 13541v1 Announce Type: cross Abstract: To overcome data scarcity and privacy constraints in data collection, it has become standard practice across academia and industry to augment real training data with text-to-image (T2I)-generated synthetic data, a paradigm we term Real-Synthetic Mix-Training (RSMT).

By Na Li, Boyu Kuang, Hongsheng Hu, Liquan Chen, Hyoungshick Kim, Yansong Gao, Anmin Fu

diffusion safety

arXiv Machine Learning

Jun 9

SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)

arXiv:2606. 08372v1 Announce Type: cross Abstract: Synthetic data is increasingly promoted as a privacy-preserving substitute for releasing sensitive tabular records, yet its central adversarial threat ("reconstruction", the recovery of an individual's hidden attribute values from a synthetic release and a handful of known quasi-identifiers) has been studied only in scattered, hard-to-compare settings.

By Steven Golob, Sikha Pentyala, Martine De Cock

benchmarks safety

arXiv AI

Jul 22

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data

arXiv:2606. 16952v2 Announce Type: replace-cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets.

By Kareem Amin, Rudrajit Das, Alessandro Epasto, Adel Javanmard, Dennis Kraft, M\'onica Ribero, Sergei Vassilvitskii

llms safety

arXiv AI

Jun 16

Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data

arXiv:2606. 16952v1 Announce Type: cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets.

By Kareem Amin, Rudrajit Das, Alessandro Epasto, Adel Javanmard, Dennis Kraft, M\'onica Ribero, Sergei Vassilvitskii

llms safety

arXiv Machine Learning

Jun 5

Quantifying the Privacy of Counterfactuals by Leveraging Membership Inference Attacks Against Synthetic Data

arXiv:2606. 06334v1 Announce Type: new Abstract: Counterfactuals are typically used in high-stakes decision areas to explain a machine learning model by showing how changes to the user profiles result in the desired outcome.

By Maryam Babaei, Yingke Wang, Hadrien Lautraite, Heber H. Arcolezi, Ulrich Aivodji, Sebastien Gambs

safety

arXiv Machine Learning

22h ago

Picture the Epsilon: Pursuing Identity-Level Privacy Guarantees for Images

arXiv:2608. 17147v1 Announce Type: cross Abstract: Image-to-image face generators are widely used, and visual dissimilarity between their outputs and source images is sometimes treated as evidence of privacy.

By Arman Zareian Jahromi, Vishnu Bondalakunta, Mohammad Akbar Bin Shah, Naimul Haque, Shuangqing Wei, George T. Amariucai

rag safety

Related stories

When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training

SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data

Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data

Quantifying the Privacy of Counterfactuals by Leveraging Membership Inference Attacks Against Synthetic Data

Picture the Epsilon: Pursuing Identity-Level Privacy Guarantees for Images