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Found 9 papers, 6 papers with code
Watermark Stealing in Large Language Models
LLM watermarking has attracted attention as a promising way to detect AI-generated content, with some works suggesting that current schemes may already be fit for deployment.
From Principle to Practice: Vertical Data Minimization for Machine Learning
We propose a novel vertical DM (vDM) workflow based on data generalization, which by design ensures that no full-resolution client data is collected during training and deployment of models, benefiting client privacy by reducing the attack surface in case of a breach.
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
Malicious server (MS) attacks have enabled the scaling of data stealing in federated learning to large batch sizes and secure aggregation, settings previously considered private.
Private and Reliable Neural Network Inference
We employ these building blocks to enable privacy-preserving NN inference with robustness and fairness guarantees in a system called Phoenix.
FARE: Provably Fair Representation Learning with Practical Certificates
To produce a practical certificate, we develop and apply a statistical procedure that computes a finite sample high-confidence upper bound on the unfairness of any downstream classifier trained on FARE embeddings.
LAMP: Extracting Text from Gradients with Language Model Priors
Recent work shows that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning.
Towards Robust Graph Contrastive Learning
We study the problem of adversarially robust self-supervised learning on graphs.
On the Paradox of Certified Training
Certified defenses based on convex relaxations are an established technique for training provably robust models.
Towards Sparse Hierarchical Graph Classifiers
Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks.
