:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wu, Hengyu, Cao, Yang
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Cryptography and Security
Online Access:	https://arxiv.org/abs/2503.19338
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Membership Inference Attacks on Sequence Models
by: Rossi, Lorenzo, et al.
Published: (2025)

Membership Inference Attacks for Unseen Classes
by: Thaker, Pratiksha, et al.
Published: (2025)

Rethinking Membership Inference Attacks Against Transfer Learning
by: Wu, Cong, et al.
Published: (2025)

Imitative Membership Inference Attack
by: Du, Yuntao, et al.
Published: (2025)

Ensembling Membership Inference Attacks Against Tabular Generative Models
by: Ward, Joshua, et al.
Published: (2025)

Membership Inference Attacks Beyond Overfitting
by: Khalil, Mona, et al.
Published: (2025)

Cascading and Proxy Membership Inference Attacks
by: Du, Yuntao, et al.
Published: (2025)

SeqMIA: Sequential-Metric Based Membership Inference Attack
by: Li, Hao, et al.
Published: (2024)

Practical Bayes-Optimal Membership Inference Attacks
by: Lassila, Marcus, et al.
Published: (2025)

Sequential Membership Inference Attacks
by: Michel, Thomas, et al.
Published: (2026)

Membership Inference Attacks and Privacy in Topic Modeling
by: Manzonelli, Nico, et al.
Published: (2024)

MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training
by: Li, Jiacheng, et al.
Published: (2023)

MRMMIA: Membership Inference Attacks on Memory in Chat Agents
by: Chen, Kai, et al.
Published: (2026)

Low-Cost High-Power Membership Inference Attacks
by: Zarifzadeh, Sajjad, et al.
Published: (2023)

Secure Aggregation is Not Private Against Membership Inference Attacks
by: Ngo, Khac-Hoang, et al.
Published: (2024)

ReproMIA: A Comprehensive Analysis of Model Reprogramming for Proactive Membership Inference Attacks
by: Huang, Chihan, et al.
Published: (2026)

Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data
by: Zhang, Jie, et al.
Published: (2024)

Membership Inference Attack with Partial Features
by: Wang, Xurun, et al.
Published: (2025)

On Membership Inference Attacks in Knowledge Distillation
by: Cui, Ziyao, et al.
Published: (2025)

A Critical Review on the Effectiveness and Privacy Threats of Membership Inference Attacks
by: Jebreel, Najeeb, et al.
Published: (2026)

Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning
by: Bai, Yuxuan, et al.
Published: (2025)

OSLO: One-Shot Label-Only Membership Inference Attacks
by: Peng, Yuefeng, et al.
Published: (2024)

Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective
by: Lian, Puwei, et al.
Published: (2025)

Noise as a Probe: Membership Inference Attacks on Diffusion Models Leveraging Initial Noise
by: Lian, Puwei, et al.
Published: (2026)

SoK: Reducing the Vulnerability of Fine-tuned Language Models to Membership Inference Attacks
by: Amit, Guy, et al.
Published: (2024)

A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data
by: Guan, Vincent, et al.
Published: (2024)

Revisiting the LiRA Membership Inference Attack Under Realistic Assumptions
by: Jebreel, Najeeb, et al.
Published: (2026)

Blind Baselines Beat Membership Inference Attacks for Foundation Models
by: Das, Debeshee, et al.
Published: (2024)

Improved Membership Inference Attacks Against Language Classification Models
by: Shachor, Shlomit, et al.
Published: (2023)

Winning the MIDST Challenge: New Membership Inference Attacks on Diffusion Models for Tabular Data Synthesis
by: Wu, Xiaoyu, et al.
Published: (2025)

Membership Inference Attacks for Retrieval Based In-Context Learning for Document Question Answering
by: Kulkarni, Tejas, et al.
Published: (2026)

Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models
by: Krčo, Nataša, et al.
Published: (2024)

VoxGuard: Evaluating User and Attribute Privacy in Speech via Membership Inference Attacks
by: Tsaprazlis, Efthymios, et al.
Published: (2025)

Automated Membership Inference Attacks: Discovering MIA Signal Computations using LLM Agents
by: Tran, Toan, et al.
Published: (2026)

Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
by: Liu, Zhenlong, et al.
Published: (2024)

Center-Based Relaxed Learning Against Membership Inference Attacks
by: Fang, Xingli, et al.
Published: (2024)

CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning
by: Chen, Depeng, et al.
Published: (2024)

Learning-Based Difficulty Calibration for Enhanced Membership Inference Attacks
by: Shi, Haonan, et al.
Published: (2024)

FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
by: Zhu, Gongxi, et al.
Published: (2024)

Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
by: Fu, Wenjie, et al.
Published: (2023)