Search Results for author:
Found 7 papers, 2 papers with code
Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective
We demonstrate that the sharer can execute fairness poisoning attacks to undermine the receiver's downstream models by manipulating the training data distribution of the diffusion model.
Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs
In this paper, we propose a novel and highly efficient solution RiseFL for secure and verifiable data collaboration, ensuring input privacy and integrity simultaneously. Firstly, we devise a probabilistic integrity check method that significantly reduces the cost of ZKP generation and verification.
Passive Inference Attacks on Split Learning via Adversarial Regularization
SDAR leverages auxiliary data and adversarial regularization to learn a decodable simulator of the client's private model, which can effectively infer the client's private features under the vanilla SL, and both features and labels under the U-shaped SL.
A Fusion-Denoising Attack on InstaHide with Data Augmentation
InstaHide is a state-of-the-art mechanism for protecting private training images, by mixing multiple private images and modifying them such that their visual features are indistinguishable to the naked eye.
Serverless Data Science -- Are We There Yet? A Case Study of Model Serving
Data scientists today have to manage the end-to-end ML life cycle that includes both model training and model serving, the latter of which is essential, as it makes their works available to end-users.
Feature Inference Attack on Model Predictions in Vertical Federated Learning
Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data collaboration without revealing their private data to each other.
Privacy Preserving Vertical Federated Learning for Tree-based Models
Federated learning (FL) is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data to each other.
