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Found 7 papers, 1 papers with code
Privacy-Preserving End-to-End Spoken Language Understanding
Thus, the SLU system needs to ensure that a potential malicious attacker cannot deduce the sensitive attributes of the users, while it should avoid greatly compromising the SLU accuracy.
Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack
Specifically, face images are masked in the frequency domain using an adaptive MixUp strategy.
Inference Attacks Against Face Recognition Model without Classification Layers
To the best of our knowledge, the proposed attack model is the very first in the literature developed for FR models without a classification layer.
A Fast, Performant, Secure Distributed Training Framework For Large Language Model
In this paper, we propose a secure distributed LLM based on model slicing.
Flatness-aware Adversarial Attack
We reveal that input regularization based methods make resultant adversarial examples biased towards flat extreme regions.
UPFL: Unsupervised Personalized Federated Learning towards New Clients
To address this challenge, we extend the adaptive risk minimization technique into the unsupervised personalized federated learning setting and propose our method, FedTTA.
You Can Backdoor Personalized Federated Learning
The resistance of pFL methods with parameter decoupling is attributed to the heterogeneous classifiers between malicious clients and benign counterparts.
