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Inference Attack

Papers

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Showing 251–283 of 283 papers

TitleStatusHype
Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs—0
Knowledge Cross-Distillation for Membership Privacy—0
Label-Only Membership Inference Attack against Node-Level Graph Neural Networks—0
On the Alignment of Group Fairness with Attribute Privacy—0
Local Model Reconstruction Attacks in Federated Learning and their Uses—0
Low-Cost High-Power Membership Inference Attacks—0
Low-Cost Privacy-Preserving Decentralized Learning—0
Machine Unlearning for Uplink Interference Cancellation—0
Machine unlearning via GAN—0
Many-Shot Regurgitation (MSR) Prompting—0
Against Membership Inference Attack: Pruning is All You Need—0
Membership Inference Attack against Long-Context Large Language Models—0
Membership Inference Attack Against Masked Image Modeling—0
Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning—0
Membership Inference Attack for Beluga Whales Discrimination—0
Membership Inference Attack in Face of Data Transformations—0
Membership Inference Attacks Against In-Context Learning—0
Membership Inference Attacks for Face Images Against Fine-Tuned Latent Diffusion Models—0
Membership Inference Attacks on Knowledge Graphs—0
Membership Inference Attacks on Sequence Models—0
Membership Inference Attack Susceptibility of Clinical Language Models—0
Membership inference attack with relative decision boundary distance—0
Membership Inference on Word Embedding and Beyond—0
Membership Privacy Evaluation in Deep Spiking Neural Networks—0
Membership Privacy Protection for Image Translation Models via Adversarial Knowledge Distillation—0
Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks—0
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers—0
ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning—0
τ: Gradient-based and Task-Agnostic machine Unlearning—0
On the Effectiveness of Regularization Against Membership Inference Attacks—0
On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel—0
On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models—0
On the Privacy Risk of In-context Learning—0
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