Image de couverture de livre

Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Liu, Terrance, Boglioni, Matteo, Fu, Yiwei, Hu, Shengyuan, Thaker, Pratiksha, Wu, Zhiwei Steven
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Cryptography and Security
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Optimizing Canaries for Privacy Auditing with Metagradient Descent
par: Boglioni, Matteo, et autres
Publié: (2025)

Membership Inference Attacks for Unseen Classes
par: Thaker, Pratiksha, et autres
Publié: (2025)

Generate-then-Verify: Reconstructing Data from Limited Published Statistics
par: Liu, Terrance, et autres
Publié: (2025)

On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift
par: Thaker, Pratiksha, et autres
Publié: (2023)

The Sample Complexity of Membership Inference and Privacy Auditing
par: Haghifam, Mahdi, et autres
Publié: (2025)

Winning the MIDST Challenge: New Membership Inference Attacks on Diffusion Models for Tabular Data Synthesis
par: Wu, Xiaoyu, et autres
Publié: (2025)

Lower Bounds for Public-Private Learning under Distribution Shift
par: Setlur, Amrith, et autres
Publié: (2025)

Privacy Auditing of Multi-domain Graph Pre-trained Model under Membership Inference Attacks
par: Luo, Jiayi, et autres
Publié: (2025)

On the Evidentiary Limits of Membership Inference for Copyright Auditing
par: Ertan, Murat Bilgehan, et autres
Publié: (2026)

Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
par: Fu, Jie, et autres
Publié: (2026)

Tight Privacy Audit in One Run
par: Xiang, Zihang, et autres
Publié: (2025)

Privacy Audit as Bits Transmission: (Im)possibilities for Audit by One Run
par: Xiang, Zihang, et autres
Publié: (2025)

Bayes-Nash Generative Privacy Against Membership Inference Attacks
par: Zhang, Tao, et autres
Publié: (2024)

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
par: Xue, Jing, et autres
Publié: (2025)

Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks
par: Fu, Haowei, et autres
Publié: (2025)

Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models
par: Wen, Yuxin, et autres
Publié: (2024)

Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
par: Liu, Zhenlong, et autres
Publié: (2024)

Auditing $f$-Differential Privacy in One Run
par: Mahloujifar, Saeed, et autres
Publié: (2024)

When Reasoning Leaks Membership: Membership Inference Attack on Black-box Large Reasoning Models
par: Hu, Ruihan, et autres
Publié: (2026)

SeqMIA: Sequential-Metric Based Membership Inference Attack
par: Li, Hao, et autres
Publié: (2024)

Unlearned but Not Forgotten: Data Extraction after Exact Unlearning in LLM
par: Wu, Xiaoyu, et autres
Publié: (2025)

United We Defend: Collaborative Membership Inference Defenses in Federated Learning
par: Bai, Li, et autres
Publié: (2026)

Membership Inference Attacks and Privacy in Topic Modeling
par: Manzonelli, Nico, et autres
Publié: (2024)

Auditing Privacy Mechanisms via Label Inference Attacks
par: Busa-Fekete, Róbert István, et autres
Publié: (2024)

Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference
par: Zhang, Chenxiang, et autres
Publié: (2025)

A Critical Review on the Effectiveness and Privacy Threats of Membership Inference Attacks
par: Jebreel, Najeeb, et autres
Publié: (2026)

OSLO: One-Shot Label-Only Membership Inference Attacks
par: Peng, Yuefeng, et autres
Publié: (2024)

Membership Inference Attacks and Defenses in Federated Learning: A Survey
par: Bai, Li, et autres
Publié: (2024)

Hide in Plain Sight: Clean-Label Backdoor for Auditing Membership Inference
par: Chen, Depeng, et autres
Publié: (2024)

How Well Can Differential Privacy Be Audited in One Run?
par: Keinan, Amit, et autres
Publié: (2025)

DiffAudit: Auditing Privacy Practices of Online Services for Children and Adolescents
par: Figueira, Olivia, et autres
Publié: (2024)

Security and Privacy Enhancing in Blockchain-based IoT Environments via Anonym Auditing
par: Khordadpour, Peyman, et autres
Publié: (2024)

NickPay, an Auditable, Privacy-Preserving, Nickname-Based Payment System
par: Quispe, Guillaume, et autres
Publié: (2025)

Membership Inference Attacks Against Video Large Language Models
par: Song, Wei, et autres
Publié: (2026)

Membership Reference Attack against Laplace Mechanism of Differential Privacy
par: Huang, Wen
Publié: (2024)

Membership Inference Attacks Against Vision-Language Models
par: Hu, Yuke, et autres
Publié: (2025)

VoxGuard: Evaluating User and Attribute Privacy in Speech via Membership Inference Attacks
par: Tsaprazlis, Efthymios, et autres
Publié: (2025)

A Bayesian Approach to Membership Inference for Statistical Release
par: Oakley, Lisa, et autres
Publié: (2026)

UniAud: A Unified Auditing Framework for High Auditing Power and Utility with One Training Run
par: Liu, Ruixuan, et autres
Publié: (2025)

OnePath: Efficient and Privacy-Preserving Decision Tree Inference in the Cloud
par: Yuan, Shuai, et autres
Publié: (2024)