Cover Image

CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Zebin, Lin, Menghan, Shen, Bolin, Anderson, Ken, Liu, Molei, Cai, Tianxi, Dong, Yushun
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks
by: Cheng, Zhan, et al.
Published: (2025)

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?
by: Zhao, Kaixiang, et al.
Published: (2026)

A Systematic Study of Model Extraction Attacks on Graph Foundation Models
by: Xu, Haoyan, et al.
Published: (2025)

Certified Defense on the Fairness of Graph Neural Networks
by: Dong, Yushun, et al.
Published: (2023)

Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses
by: Li, Lincan, et al.
Published: (2025)

A Survey on Model Extraction Attacks and Defenses for Large Language Models
by: Zhao, Kaixiang, et al.
Published: (2025)

A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments
by: Zhao, Kaixiang, et al.
Published: (2025)

Efficient and Effective Model Extraction
by: Zhu, Hongyu, et al.
Published: (2024)

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
by: Zhao, Kaixiang, et al.
Published: (2025)

MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models
by: Cheng, Xueqi, et al.
Published: (2025)

DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning
by: Zhao, Kaixiang, et al.
Published: (2025)

Hard-Label Cryptanalytic Extraction of Neural Network Models
by: Chen, Yi, et al.
Published: (2024)

FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework
by: Cai, Jinyu, et al.
Published: (2024)

Panther: A Cost-Effective Privacy-Preserving Framework for GNN Training and Inference Services in Cloud Environments
by: Chen, Congcong, et al.
Published: (2025)

PROVGEN: A Privacy-Preserving Approach for Outcome Validation in Genomic Research
by: Jiang, Yuzhou, et al.
Published: (2022)

Model Extraction Attacks Revisited
by: Liang, Jiacheng, et al.
Published: (2023)

CaBaGe: Data-Free Model Extraction using ClAss BAlanced Generator Ensemble
by: Rosenthal, Jonathan, et al.
Published: (2024)

On the Effectiveness of Membership Inference in Targeted Data Extraction from Large Language Models
by: Sahili, Ali Al, et al.
Published: (2025)

Efficient Model Extraction via Boundary Sampling
by: Dor, Maor Biton, et al.
Published: (2024)

Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks
by: Dong, Xuewen, et al.
Published: (2025)

Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
by: Wang, Xiaobao, et al.
Published: (2025)

Is the Hard-Label Cryptanalytic Model Extraction Really Polynomial?
by: Ito, Akira, et al.
Published: (2025)

SAFEx: Analyzing Vulnerabilities of MoE-Based LLMs via Stable Safety-critical Expert Identification
by: Lai, Zhenglin, et al.
Published: (2025)

Less is More: Revisiting the Gaussian Mechanism for Differential Privacy
by: Ji, Tianxi, et al.
Published: (2023)

Effective Universal Unrestricted Adversarial Attacks using a MOE Approach
by: Baia, A. E., et al.
Published: (2021)

LoRAGuard: An Effective Black-box Watermarking Approach for LoRAs
by: Lv, Peizhuo, et al.
Published: (2025)

Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation
by: Qi, Zhisheng, et al.
Published: (2026)

From Counterfactuals to Trees: Competitive Analysis of Model Extraction Attacks
by: Khouna, Awa, et al.
Published: (2025)

DMRL: Data- and Model-aware Reward Learning for Data Extraction
by: Wang, Zhiqiang, et al.
Published: (2025)

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
by: Buchholz, Erik, et al.
Published: (2025)

AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models
by: Oksuz, Abdullah Caglar, et al.
Published: (2023)

ProDiF: Protecting Domain-Invariant Features to Secure Pre-Trained Models Against Extraction
by: Zhou, Tong, et al.
Published: (2025)

MEA-Defender: A Robust Watermark against Model Extraction Attack
by: Lv, Peizhuo, et al.
Published: (2024)

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective
by: Liu, Chang, et al.
Published: (2025)

Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations
by: Li, Jiate, et al.
Published: (2025)

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

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

Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles
by: Zhang, Cui, et al.
Published: (2024)

FDINet: Protecting against DNN Model Extraction via Feature Distortion Index
by: Yao, Hongwei, et al.
Published: (2023)

Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey
by: Ye, Mang, et al.
Published: (2024)