Papers Inference Attack
“Inference Attack” 태그가 달린 논문 283편 · 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 AttackRectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning ensures complete data removal equivalent t…
Inference AttackMachine UnlearningApollo: 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 AttackEC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
Graph Neural Networks (GNNs) have been widely used for graph analysis. Federated Graph Learning (FGL) is an emerging learning framework to collaboratively train graph data from various clients. However, since clients are…
Graph LearningInference AttackLink PredictionNode ClassificationSecuring 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 AttackWhispers of Data: Unveiling Label Distributions in Federated Learning Through Virtual Client Simulation
Federated Learning enables collaborative training of a global model across multiple geographically dispersed clients without the need for data sharing. However, it is susceptible to inference attacks, particularly label …
Federated LearningInference AttackDeSIA: Attribute Inference Attacks Against Limited Fixed Aggregate Statistics
Empirical inference attacks are a popular approach for evaluating the privacy risk of data release mechanisms in practice. While an active attack literature exists to evaluate machine learning models or synthetic data re…
AttributeInference AttackDisparate Privacy Vulnerability: Targeted Attribute Inference Attacks and Defenses
As machine learning (ML) technologies become more prevalent in privacy-sensitive areas like healthcare and finance, eventually incorporating sensitive information in building data-driven algorithms, it is vital to scruti…
AttributeInference 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 AttackPredictionBLIA: Detect model memorization in binary classification model through passive Label Inference attack
Model memorization has implications for both the generalization capacity of machine learning models and the privacy of their training data. This paper investigates label memorization in binary classification models throu…
Binary ClassificationInference AttackMemorizationmodelDP-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 Attack