← Search

Emiliano De Cristofaro

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
2024

Nearly Tight Black-Box Auditing of Differentially Private Machine Learning

NeurIPS 2024poster

This paper presents an auditing procedure for the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box threat model that is substantially tighter than prior work. The main intuition is to craft worst-case initial model parameters, as DP-SGD's privacy analysis is agn…

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…