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Georgi Ganev

3 accepted papers

2026

SMOTE and Mirrors: Exposing Privacy Leakage from Synthetic Minority Oversampling

ICLR 2026poster

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, little attention has been paid to its privacy implications; yet, it is used in the wild in many privacy-sensitive applic…

Cited by 0SourcecodeScholar
2023

Synthetic Data Generation of Many-to-Many Datasets via Random Graph Generation

ICLR 2023poster

Synthetic data generation (SDG) has become a popular approach to release private datasets. In SDG, a generative model is fitted on the private real data, and samples drawn from the model are released as the protected synthetic data. While real-world datasets usually consist of multiple tables with p…

Cited by 6SourcePDFScholar
2022

Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data

ICML 2022spotlight

Generative models trained with Differential Privacy (DP) can be used to generate synthetic data while minimizing privacy risks. We analyze the impact of DP on these models vis-a-vis underrepresented classes/subgroups of data, specifically, studying: 1) the size of classes/subgroups in the synthetic…