Study Finds SMOTE Generates Significant Privacy Leakage

Global: Study Finds SMOTE Generates Significant Privacy Leakage

A recent preprint demonstrates that the Synthetic Minority Over-sampling Technique (SMOTE), a widely adopted method for handling class imbalance, can expose sensitive information about minority records. According to the authors, membership inference attacks achieve high accuracy against datasets augmented with SMOTE, challenging the assumption that the technique is privacy‑neutral.

Background

SMOTE has become a standard tool in machine learning pipelines that require synthetic data to balance skewed class distributions. While its effectiveness for improving model performance is well documented, the literature has paid limited attention to potential privacy risks associated with the generated samples.

Methodology

The researchers evaluated conventional privacy metrics—naïve distinguishing and distance‑to‑closest‑record—and found them insufficient for detecting leakage. They then implemented membership inference attacks (MIAs) that exploit SMOTE’s geometric properties. Two novel attacks, named DistinSMOTE and ReconSMOTE, were introduced. DistinSMOTE perfectly separates real from synthetic records in augmented datasets, while ReconSMOTE reconstructs original minority records with precision and recall approaching one under realistic imbalance ratios.

Key Findings

Experimental results on eight standard imbalanced datasets confirm the practicality of both attacks. DistinSMOTE achieved a 100% true‑positive rate in distinguishing real versus synthetic entries. ReconSMOTE succeeded in reconstructing minority records with near‑perfect accuracy, demonstrating that SMOTE can inadvertently reveal the underlying data it seeks to protect.

Implications

The study indicates that SMOTE is inherently non‑private, disproportionately exposing minority records compared to majority classes. This raises concerns for applications where synthetic data are shared or published, especially in domains such as healthcare, finance, or any context involving personally identifiable information.

Recommendations

The authors suggest reevaluating the use of SMOTE in privacy‑sensitive settings and considering alternative techniques that incorporate differential privacy guarantees or other formal privacy protections.

Future Work

Further research is called for to quantify privacy risks across a broader range of synthetic data generation methods and to develop mitigation strategies that balance utility with robust privacy safeguards.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.