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Inference Attack

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Showing 151–200 of 283 papers

TitleStatusHype
Prompt-based Unifying Inference Attack on Graph Neural Networks—0
Property inference attack; Graph neural networks; Privacy attacks and defense; Trustworthy machine learning—0
Purifier: Defending Data Inference Attacks via Transforming Confidence Scores—0
Quantifying (Hyper) Parameter Leakage in Machine Learning—0
Quantifying Membership Inference Vulnerability via Generalization Gap and Other Model Metrics—0
Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks—0
Ranking Differential Privacy—0
ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods—0
Membership Privacy for Machine Learning Models Through Knowledge Transfer—0
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense—0
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach—0
Reducing audio membership inference attack accuracy to chance: 4 defenses—0
Residue-based Label Protection Mechanisms in Vertical Logistic Regression—0
Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries—0
Securing Genomic Data Against Inference Attacks in Federated Learning Environments—0
See through Gradients: Image Batch Recovery via GradInversion—0
Semantic Membership Inference Attack against Large Language Models—0
Set-Membership Inference Attacks using Data Watermarking—0
Sharing Models or Coresets: A Study based on Membership Inference Attack—0
Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning—0
Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack—0
Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge—0
Task Contamination: Language Models May Not Be Few-Shot Anymore—0
TDDBench: A Benchmark for Training data detection—0
Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment—0
Quality Inference in Federated Learning with Secure Aggregation—0
Tool Unlearning for Tool-Augmented LLMs—0
Towards Black-Box Membership Inference Attack for Diffusion Models—0
Towards Label-Only Membership Inference Attack against Pre-trained Large Language Models—0
Towards More Realistic Membership Inference Attacks on Large Diffusion Models—0
Towards Reliable Empirical Machine Unlearning Evaluation: A Game-Theoretic View—0
Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models—0
On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks—0
Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning—0
Unlearning Clients, Features and Samples in Vertical Federated Learning—0
Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation—0
Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data—0
Unveiling Structural Memorization: Structural Membership Inference Attack for Text-to-Image Diffusion Models—0
Unveiling Synthetic Faces: How Synthetic Datasets Can Expose Real Identities—0
User-Level Membership Inference Attack against Metric Embedding Learning—0
VertexSerum: Poisoning Graph Neural Networks for Link Inference—0
WaKA: Data Attribution using K-Nearest Neighbors and Membership Privacy Principles—0
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning—0
Whispers of Data: Unveiling Label Distributions in Federated Learning Through Virtual Client Simulation—0
White-box Inference Attacks against Centralized Machine Learning and Federated Learning—0
White-box Membership Attack Against Machine Learning Based Retinopathy Classification—0
Machine Unlearning for Uplink Interference Cancellation—0
Machine unlearning via GAN—0
Many-Shot Regurgitation (MSR) Prompting—0
Against Membership Inference Attack: Pruning is All You Need—0
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