Search Results for author:
Found 5 papers, 2 papers with code
Information Flow Control in Machine Learning through Modular Model Architecture
In today's machine learning (ML) models, any part of the training data can affect its output.
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100, 000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy.
Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems
This paper explores the private information that may be learned by tracking a recommendation model's sparse feature access patterns.
MPCViT: Searching for Accurate and Efficient MPC-Friendly Vision Transformer with Heterogeneous Attention
Secure multi-party computation (MPC) enables computation directly on encrypted data and protects both data and model privacy in deep learning inference.
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis
Federated learning (FL) aims to perform privacy-preserving machine learning on distributed data held by multiple data owners.
