Cover Image

The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ganev, Georgi, De Cristofaro, Emiliano
Format:	Preprint
Published:	2023
Subjects:	Cryptography and Security Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

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

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

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

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

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

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

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

SMOTE and Mirrors: Exposing Privacy Leakage from Synthetic Minority Oversampling
by: Ganev, Georgi, et al.
Published: (2025)

Multi-class Classifier based Failure Prediction with Artificial and Anonymous Training for Data Privacy
by: Das, Dibakar, et al.
Published: (2022)

Synthetic Data: Methods, Use Cases, and Risks
by: De Cristofaro, Emiliano
Published: (2023)

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

AI-Driven Anonymization: Protecting Personal Data Privacy While Leveraging Machine Learning
by: Yang, Le, et al.
Published: (2024)

Similarity-based Label Inference Attack against Training and Inference of Split Learning
by: Liu, Junlin, et al.
Published: (2022)

RL-Finetuned LLMs for Privacy-Preserving Synthetic Rewriting
by: Shi, Zhan, et al.
Published: (2025)

Does Training with Synthetic Data Truly Protect Privacy?
by: Zhao, Yunpeng, et al.
Published: (2025)

When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks
by: Tian, Huan, et al.
Published: (2023)

KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation
by: Kotal, Anantaa, et al.
Published: (2024)

How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers
by: Zhang, Guangsheng, et al.
Published: (2022)

Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection
by: Lange, Lucas, et al.
Published: (2024)

IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization
by: Frikha, Ahmed, et al.
Published: (2024)

Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks?
by: Du, Hao, et al.
Published: (2025)

Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning
by: Ahamed, Sayyed Farid, et al.
Published: (2024)

A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures
by: Nguyen, Thanh Tam, et al.
Published: (2024)

Robust Privacy: Inference-Time Privacy through Certified Robustness
by: Jin, Jiankai, et al.
Published: (2026)

Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
by: Diana, Francesco, et al.
Published: (2025)

Beyond Data Privacy: New Privacy Risks for Large Language Models
by: Du, Yuntao, et al.
Published: (2025)

Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy
by: Kaiser, Johannes, et al.
Published: (2026)

LinkThief: Combining Generalized Structure Knowledge with Node Similarity for Link Stealing Attack against GNN
by: Zhang, Yuxing, et al.
Published: (2024)

Privacy Bias in Language Models: A Contextual Integrity-based Auditing Metric
by: Shvartzshnaider, Yan, et al.
Published: (2024)

Privacy-Preserving in Blockchain-based Federated Learning Systems
by: M., Sameera K., et al.
Published: (2024)

RAG with Differential Privacy
by: Grislain, Nicolas
Published: (2024)

Privacy Auditing of Multi-domain Graph Pre-trained Model under Membership Inference Attacks
by: Luo, Jiayi, et al.
Published: (2025)

Attack-Aware Noise Calibration for Differential Privacy
by: Kulynych, Bogdan, et al.
Published: (2024)

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
by: Ponomareva, Natalia, et al.
Published: (2025)

A Consensus Privacy Metrics Framework for Synthetic Data
by: Pilgram, Lisa, et al.
Published: (2025)

Evaluating Privacy Leakage in Split Learning
by: Qiu, Xinchi, et al.
Published: (2023)

Privacy-Preserving Dynamic Assortment Selection
by: Cho, Young Hyun, et al.
Published: (2024)

Privacy Auditing of Large Language Models
by: Panda, Ashwinee, et al.
Published: (2025)