9 citations · 31 across the 18 of their papers we have counts for
5 papers · 1 filter
SMOTE and Mirrors: Exposing Privacy Leakage from Synthetic Minority Oversampling
Georgi Ganev, Reza Nazari, Rees Davison +5
The Synthetic Minority Over-sampling Technique (SMOTE) is one of the most widely used methods for addressing class imbalance and generating synthetic data. Despite its popularity,…
dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
Sofiane Mahiou, Amir Dizche, Reza Nazari +4
We propose dpmm, an open-source library for synthetic data generation with Differentially Private (DP) guarantees. It includes three popular marginal models -- PrivBayes, MST, and…
The DCR Delusion: Measuring the Privacy Risk of Synthetic Data
Zexi Yao, Nataša Krčo, Georgi Ganev +1
Synthetic data has become an increasingly popular way to share data without revealing sensitive information. Though Membership Inference Attacks (MIAs) are widely considered the go…
Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy
Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Sofiane Mahiou +1
Privacy attacks, particularly membership inference attacks (MIAs), are widely used to assess the privacy of generative models for tabular synthetic data, including those with Diffe…
The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data
Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Sofiane Mahiou +1
Differentially Private (DP) generative marginal models are often used in the wild to release synthetic tabular datasets in lieu of sensitive data while providing formal privacy gua…