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Found 5 papers, 1 papers with code
MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference
Multi-party computing (MPC) has been gaining popularity over the past years as a secure computing model, particularly for machine learning (ML) inference.
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.
