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Inference Attack

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Showing 126–150 of 283 papers

TitleStatusHype
Towards Reliable Empirical Machine Unlearning Evaluation: A Game-Theoretic View—0
Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning—0
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures—0
τ: Gradient-based and Task-Agnostic machine Unlearning—0
Low-Cost Privacy-Preserving Decentralized Learning—0
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference AttacksCode0
On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks—0
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?—0
Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning—0
De-identification is not always enough—0
Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation—0
Inference Attacks Against Face Recognition Model without Classification Layers—0
Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation—0
Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software DeploymentCode0
Task Contamination: Language Models May Not Be Few-Shot Anymore—0
User Consented Federated Recommender System Against Personalized Attribute Inference AttackCode0
Adaptive Domain Inference Attack with Concept Hierarchy—0
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding—0
Low-Cost High-Power Membership Inference Attacks—0
DUCK: Distance-based Unlearning via Centroid KinematicsCode0
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated LearningCode0
MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated LearningCode0
Are Normalizing Flows the Key to Unlocking the Exponential Mechanism?Code0
Preserving Privacy in GANs Against Membership Inference Attack—0
Generated Distributions Are All You Need for Membership Inference Attacks Against Generative ModelsCode0
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