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Inference Attack

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Showing 251–283 of 283 papers

TitleStatusHype
Against Membership Inference Attack: Pruning is All You Need—0
ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning—0
Quality Inference in Federated Learning with Secure Aggregation—0
Sharing Models or Coresets: A Study based on Membership Inference Attack—0
Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers—0
On the Effectiveness of Regularization Against Membership Inference Attacks—0
DAMIA: Leveraging Domain Adaptation as a Defense against Membership Inference Attacks—0
Defending Model Inversion and Membership Inference Attacks via Prediction Purification—0
Data and Model Dependencies of Membership Inference AttackCode0
Differentially Private k-Means Clustering with Guaranteed Convergence—0
Membership Inference Attacks Against Object Detection ModelsCode0
Assessing differentially private deep learning with Membership InferenceCode0
An Empirical Study on the Intrinsic Privacy of SGDCode0
Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability—0
RIGA: Covert and Robust White-Box Watermarking of Deep Neural NetworksCode0
Quantifying (Hyper) Parameter Leakage in Machine Learning—0
Reducing audio membership inference attack accuracy to chance: 4 defenses—0
Eavesdrop the Composition Proportion of Training Labels in Federated Learning—0
Adversarial Privacy Preservation under Attribute Inference Attack—0
Defending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges—0
GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative ModelsCode0
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation—0
Membership Privacy for Machine Learning Models Through Knowledge Transfer—0
Reconstruction and Membership Inference Attacks against Generative ModelsCode0
Disparate Vulnerability to Membership Inference AttacksCode0
Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models—0
Privacy Risks of Securing Machine Learning Models against Adversarial ExamplesCode0
Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data—0
Differentially Private Data Generative Models—0
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls—0
TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks—0
Understanding Membership Inferences on Well-Generalized Learning ModelsCode0
Membership Inference Attacks against Machine Learning ModelsCode0
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