Search Results for author:
Found 5 papers, 1 papers with code
MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference
In this work, we show that serialization is unnecessary, particularly in the context of ML computations (both in Convolutional neural networks and in Transformer-based models).
DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware
DarKnight relies on cooperative execution between trusted execution environments (TEE) and accelerators, where the TEE provides privacy and integrity verification, while accelerators perform the bulk of the linear algebraic computation to optimize the performance.
Byzantine-Robust and Privacy-Preserving Framework for FedML
This learning setting presents, among others, two unique challenges: how to protect privacy of the clients' data during training, and how to ensure integrity of the trained model.
Privacy and Integrity Preserving Training Using Trusted Hardware
Privacy and security-related concerns are growing as machine learning reaches diverse application domains.
Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments
However, the overhead of blinding and unblinding the data is a limiting factor to scalability.
