Papers Membership Inference Attack
“Membership Inference Attack” 태그가 달린 논문 186편 · 필터 해제
Orthogonal Soft Pruning for Efficient Class Unlearning
Machine unlearning aims to selectively remove class-specific knowledge from pretrained neural networks to satisfy privacy regulations such as the GDPR. Existing methods typically face a trade-off between unlearning speed…
Inference AttackMachine UnlearningMembership Inference AttackApollo: A Posteriori Label-Only Membership Inference Attack Towards Machine Unlearning
Machine Unlearning (MU) aims to update Machine Learning (ML) models following requests to remove training samples and their influences on a trained model efficiently without retraining the original ML model from scratch.…
Inference AttackMachine UnlearningMembership Inference AttackWhen Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning
With the rapid advancement of deep learning technology, pre-trained encoder models have demonstrated exceptional feature extraction capabilities, playing a pivotal role in the research and application of deep learning. H…
Contrastive LearningInference AttackMembership Inference AttackSelf-Supervised LearningMembership Inference Attacks on Sequence Models
Sequence models, such as Large Language Models (LLMs) and autoregressive image generators, have a tendency to memorize and inadvertently leak sensitive information. While this tendency has critical legal implications, ex…
Inference AttackMembership Inference AttackMemorizationAn Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks
Graph Neural Network-based methods face privacy leakage risks due to the introduction of topological structures about the targets, which allows attackers to bypass the target's prior knowledge of the sensitive attributes…
DiversityGraph Neural NetworkInference AttackMembership Inference AttackSecuring Genomic Data Against Inference Attacks in Federated Learning Environments
Federated Learning (FL) offers a promising framework for collaboratively training machine learning models across decentralized genomic datasets without direct data sharing. While this approach preserves data locality, it…
Federated LearningInference AttackMembership Inference AttackPrivacy PreservingAugMixCloak: A Defense against Membership Inference Attacks via Image Transformation
Traditional machine learning (ML) raises serious privacy concerns, while federated learning (FL) mitigates the risk of data leakage by keeping data on local devices. However, the training process of FL can still leak sen…
Data AugmentationFederated LearningInference AttackMembership Inference AttackA new membership inference attack that spots memorization in generative and predictive models: Loss-Based with Reference Model algorithm (LBRM)
Generative models can unintentionally memorize training data, posing significant privacy risks. This paper addresses the memorization phenomenon in time series imputation models, introducing the Loss-Based with Reference…
ImputationInference AttackMembership Inference AttackMemorization+1Automatic Calibration for Membership Inference Attack on Large Language Models
Membership Inference Attacks (MIAs) have recently been employed to determine whether a specific text was part of the pre-training data of Large Language Models (LLMs). However, existing methods often misinfer non-members…
Inference AttackMembership Inference AttackGraph-Level Label-Only Membership Inference Attack against Graph Neural Networks
Graph neural networks (GNNs) are widely used for graph-structured data but are vulnerable to membership inference attacks (MIAs) in graph classification tasks, which determine if a graph was part of the training dataset,…
Graph ClassificationInference AttackMembership Inference AttackPredictionDP-GPL: Differentially Private Graph Prompt Learning
Graph Neural Networks (GNNs) have shown remarkable performance in various applications. Recently, graph prompt learning has emerged as a powerful GNN training paradigm, inspired by advances in language and vision foundat…
Inference AttackMembership Inference AttackPrompt LearningEfficient Membership Inference Attacks by Bayesian Neural Network
Membership Inference Attacks (MIAs) aim to estimate whether a specific data point was used in the training of a given model. Previous attacks often utilize multiple reference models to approximate the conditional score d…
Bayesian InferenceInference AttackMembership Inference Attackquantile regressionTowards Label-Only Membership Inference Attack against Pre-trained Large Language Models
Membership Inference Attacks (MIAs) aim to predict whether a data sample belongs to the model's training set or not. Although prior research has extensively explored MIAs in Large Language Models (LLMs), they typically r…
Inference AttackMembership Inference AttackSemantic SimilaritySemantic Textual SimilarityMembership Inference Attacks for Face Images Against Fine-Tuned Latent Diffusion Models
The rise of generative image models leads to privacy concerns when it comes to the huge datasets used to train such models. This paper investigates the possibility of inferring if a set of face images was used for fine-t…
Inference AttackMembership Inference AttackA hierarchical approach for assessing the vulnerability of tree-based classification models to membership inference attack
Machine learning models can inadvertently expose confidential properties of their training data, making them vulnerable to membership inference attacks (MIA). While numerous evaluation methods exist, many require computa…
Inference AttackMembership Inference AttackDocMIA: Document-Level Membership Inference Attacks against DocVQA Models
Document Visual Question Answering (DocVQA) has introduced a new paradigm for end-to-end document understanding, and quickly became one of the standard benchmarks for multimodal LLMs. Automating document processing workf…
document understandingInference AttackMembership Inference AttackQuestion Answering+1Privacy Attacks on Image AutoRegressive Models
Image autoregressive (IAR) models have surpassed diffusion models (DMs) in both image quality (FID: 1.48 vs. 1.58) and generation speed. However, their privacy risks remain largely unexplored. To address this, we conduct…
Inference AttackMembership Inference AttackTool Unlearning for Tool-Augmented LLMs
Tool-augmented large language models (LLMs) are often trained on datasets of query-response pairs, which embed the ability to use tools or APIs directly into the parametric knowledge of LLMs. Tool-augmented LLMs need the…
Inference AttackMembership Inference AttackRiddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to generate grounded responses by leveraging external knowledge databases without altering model parameters. Although the absence of weight tuning…
Membership Inference AttackRAGRetrievalRetrieval-augmented GenerationRedefining Machine Unlearning: A Conformal Prediction-Motivated Approach
Machine unlearning seeks to remove the influence of specified data from a trained model. While metrics such as unlearning accuracy (UA) and membership inference attack (MIA) provide baselines for assessing unlearning per…
Adversarial AttackConformal Predictionimage-classificationImage Classification+5