HASSLE combines gradient-based label inference with self-supervised pretraining and adversarial embeddings to hijack vertical federated learning models, achieving over 99% attack success on four datasets and 85% on CIFAR-100.
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HASSLE: A Self-Supervised Learning Enhanced Hijacking Attack on Vertical Federated Learning
HASSLE combines gradient-based label inference with self-supervised pretraining and adversarial embeddings to hijack vertical federated learning models, achieving over 99% attack success on four datasets and 85% on CIFAR-100.