Imagen de Portada

SMOTE and Mirrors: Exposing Privacy Leakage from Synthetic Minority Oversampling

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Ganev, Georgi, Nazari, Reza, Davison, Rees, Dizche, Amir, Wu, Xinmin, Abbey, Ralph, Silva, Jorge, De Cristofaro, Emiliano
Formato:	Preprint
Publicado:	2025
Materias:	Cryptography and Security
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
por: Mahiou, Sofiane, et al.
Publicado: (2025)

The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets
por: Ganev, Georgi, et al.
Publicado: (2023)

Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective
por: Ganev, Georgi, et al.
Publicado: (2026)

Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy
por: Ganev, Georgi, et al.
Publicado: (2025)

"What do you want from theory alone?" Experimenting with Tight Auditing of Differentially Private Synthetic Data Generation
por: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Publicado: (2024)

Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility
por: Ganev, Georgi, et al.
Publicado: (2023)

Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliance
por: Ganev, Georgi
Publicado: (2024)

The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data
por: Ganev, Georgi, et al.
Publicado: (2025)

The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging
por: Ganev, Georgi, et al.
Publicado: (2024)

DP-SMOTE: Integrating Differential Privacy and Oversampling Technique to Preserve Privacy in Smart Homes
por: Elsayed, Amr Tarek, et al.
Publicado: (2025)

Synthetic Data: Methods, Use Cases, and Risks
por: De Cristofaro, Emiliano
Publicado: (2023)

The DCR Delusion: Measuring the Privacy Risk of Synthetic Data
por: Yao, Zexi, et al.
Publicado: (2025)

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
por: Zhou, Yan, et al.
Publicado: (2025)

Tight Auditing of Differential Privacy in MST and AIM
por: Ganev, Georgi, et al.
Publicado: (2026)

What's on Your Mind? Exploring Privacy of Mental Health Apps
por: Georgiou, Chloe, et al.
Publicado: (2026)

CLIOPATRA: Extracting Private Information from LLM Insights
por: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Publicado: (2026)

A Systematic Review of Federated Generative Models
por: Gargary, Ashkan Vedadi, et al.
Publicado: (2024)

Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
por: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Publicado: (2024)

PROTEAN: Federated Intrusion Detection in Non-IID Environments through Prototype-Based Knowledge Sharing
por: Chennoufi, Sara, et al.
Publicado: (2025)

Improving IoT Intrusion Detection Through SMOTE-Based Oversampling and Extended Multi-Model Evaluation on Side-Channel Power Data
por: Shahzad, Muhammad Khuram, et al.
Publicado: (2026)

Exposing and Defending Membership Leakage in Vulnerability Prediction Models
por: Liao, Yihan, et al.
Publicado: (2025)

Exposed: Shedding Blacklight on Online Privacy
por: Shen, Lucas, et al.
Publicado: (2025)

CoLA: A Choice Leakage Attack Framework to Expose Privacy Risks in Subset Training
por: Li, Qi, et al.
Publicado: (2026)

Beyond the Crawl: Unmasking Browser Fingerprinting in Real User Interactions
por: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Publicado: (2025)

Understanding AI Methods for Intrusion Detection and Cryptographic Leakage
por: Zilouchian, Reza, et al.
Publicado: (2026)

Computing Maximal Per-Record Leakage and Leakage-Distortion Functions for Privacy Mechanisms under Entropy-Constrained Adversaries
por: Wu, Genqiang, et al.
Publicado: (2026)

To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling
por: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Publicado: (2024)

Driving Privacy Forward: Mitigating Information Leakage within Smart Vehicles through Synthetic Data Generation
por: Parikh, Krish
Publicado: (2024)

Comparative Evaluation of VAE, GAN, and SMOTE for Tor Detection in Encrypted Network Traffic
por: A, Saravanan, et al.
Publicado: (2026)

CRFU: Compressive Representation Forgetting Against Privacy Leakage on Machine Unlearning
por: Wang, Weiqi, et al.
Publicado: (2025)

PEEL: A Poisoning-Exposing Encoding Theoretical Framework for Local Differential Privacy
por: Shuai, Lisha, et al.
Publicado: (2025)

PrivTru: A Privacy-by-Design Data Trustee Minimizing Information Leakage
por: Gehring, Lukas, et al.
Publicado: (2025)

Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
por: Fu, Jie, et al.
Publicado: (2026)

Observable Channels, Not Just Storage: Evaluating Privacy Leakage in LLM Agent Pipelines
por: Huang, Tao, et al.
Publicado: (2026)

Exposing LLM User Privacy via Traffic Fingerprint Analysis: A Study of Privacy Risks in LLM Agent Interactions
por: Zhang, Yixiang, et al.
Publicado: (2025)

EdgeLeakage: Membership Information Leakage in Distributed Edge Intelligence Systems
por: Chen, Kongyang, et al.
Publicado: (2024)

TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities
por: Saeed, Mohammad Hammas, et al.
Publicado: (2023)

The Hidden Cost of Correlation: Rethinking Privacy Leakage in Local Differential Privacy
por: Jayawardana, Sandaru, et al.
Publicado: (2025)

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage
por: Meng, Jiayang, et al.
Publicado: (2025)

Cross-Flow Correlations Survive Synthesis: Measuring Source-Level Privacy Leakage in Synthetic Network Traces
por: Jin, Minhao, et al.
Publicado: (2025)