Papers Reconstruction Attack
“Reconstruction Attack” 태그가 달린 논문 68편 · 필터 해제
DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences
Federated learning enables collaborative machine learning while preserving data privacy. However, the rise of federated unlearning, designed to allow clients to erase their data from the global model, introduces new priv…
Federated LearningReconstruction AttackByzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates
Federated learning (FL) enables decentralized machine learning without sharing raw data, allowing multiple clients to collaboratively learn a global model. However, studies reveal that privacy leakage is possible under c…
Federated LearningReconstruction AttackTimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems
Federated Learning has emerged as a privacy-oriented alternative to centralized Machine Learning, enabling collaborative model training without direct data sharing. While extensively studied for neural networks, the secu…
Federated LearningPrivacy PreservingReconstruction AttackReconXF: Graph Reconstruction Attack via Public Feature Explanations on Privatized Node Features and Labels
Graph Neural Networks (GNNs) achieve high performance across many applications but function as black-box models, limiting their use in critical domains like healthcare and criminal justice. Explainability methods address…
DenoisingGraph ReconstructionReconstruction AttackCutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
Federated Learning (FL) enables collaborative training of machine learning models across distributed clients without sharing raw data, ostensibly preserving data privacy. Nevertheless, recent studies have revealed critic…
Federated LearningReconstruction AttackVVRec: Reconstruction Attacks on DL-based Volumetric Video Upstreaming via Latent Diffusion Model with Gamma Distribution
With the popularity of 3D volumetric video applications, such as Autonomous Driving, Virtual Reality, and Mixed Reality, current developers have turned to deep learning for compressing volumetric video frames, i.e., poin…
Autonomous DrivingMixed RealityReconstruction AttackVideo ReconstructionLocal Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
Reconstruction attacks against federated learning (FL) aim to reconstruct users' samples through users' uploaded gradients. Local differential privacy (LDP) is regarded as an effective defense against various attacks, in…
DenoisingFederated LearningReconstruction AttackLATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation
The privacy vulnerabilities of the federated learning (FL) paradigm, primarily caused by gradient leakage, have prompted the development of various defensive measures. Nonetheless, these solutions have predominantly been…
AttributeFederated LearningPrivacy PreservingReconstruction AttackTraining Set Reconstruction from Differentially Private Forests: How Effective is DP?
Recent research has shown that machine learning models are vulnerable to privacy attacks targeting their training data. Differential privacy (DP) has become a widely adopted countermeasure, as it offers rigorous privacy …
Reconstruction AttackA Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis
The proliferation of healthcare data has expanded opportunities for collaborative research, yet stringent privacy regulations hinder pooling sensitive patient records. We propose a \emph{multiparty homomorphic encryption…
Privacy PreservingReconstruction AttackSurvival AnalysisHidden Data Privacy Breaches in Federated Learning
Federated Learning (FL) emerged as a paradigm for conducting machine learning across broad and decentralized datasets, promising enhanced privacy by obviating the need for direct data sharing. However, recent studies sho…
Federated LearningReconstruction AttackFace Reconstruction from Face Embeddings using Adapter to a Face Foundation Model
Face recognition systems extract embedding vectors from face images and use these embeddings to verify or identify individuals. Face reconstruction attack (also known as template inversion) refers to reconstructing face …
Face RecognitionFace ReconstructionReconstruction AttackInvestigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack
This study investigates privacy leakage in dimensionality reduction methods through a novel machine learning-based reconstruction attack. Employing an informed adversary threat model, we develop a neural network capable …
Dimensionality ReductionReconstruction AttackSupervised and Unsupervised Alignments for Spoofing Behavioral Biometrics
Biometric recognition systems are security systems based on intrinsic properties of their users, usually encoded in high dimension representations called embeddings, which potential theft would represent a greater threat…
Reconstruction AttackSpeaker VerificationUnveiling Hidden Visual Information: A Reconstruction Attack Against Adversarial Visual Information Hiding
This paper investigates the security vulnerabilities of adversarial-example-based image encryption by executing data reconstruction (DR) attacks on encrypted images. A representative image encryption method is the advers…
Reconstruction AttackFace Reconstruction Transfer Attack as Out-of-Distribution Generalization
Understanding the vulnerability of face recognition systems to malicious attacks is of critical importance. Previous works have focused on reconstructing face images that can penetrate a targeted verification system. Eve…
Face RecognitionFace ReconstructionOut-of-Distribution GeneralizationReconstruction AttackBayes' capacity as a measure for reconstruction attacks in federated learning
Within the machine learning community, reconstruction attacks are a principal attack of concern and have been identified even in federated learning, which was designed with privacy preservation in mind. In federated lear…
Federated LearningReconstruction AttackUIFV: Data Reconstruction Attack in Vertical Federated Learning
Vertical Federated Learning (VFL) facilitates collaborative machine learning without the need for participants to share raw private data. However, recent studies have revealed privacy risks where adversaries might recons…
Federated LearningReconstruction AttackVertical Federated LearningIs Diffusion Model Safe? Severe Data Leakage via Gradient-Guided Diffusion Model
Gradient leakage has been identified as a potential source of privacy breaches in modern image processing systems, where the adversary can completely reconstruct the training images from leaked gradients. However, existi…
Image ReconstructionmodelReconstruction AttackURVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning
Launching effective malicious attacks in VFL presents unique challenges: 1) Firstly, given the distributed nature of clients' data features and models, each client rigorously guards its privacy and prohibits direct query…
Federated LearningReconstruction AttackVertical Federated Learning