confidential computing

Encryption covers data at rest and in transit, but the moment you compute on it the data sits in cleartext in memory. Confidential computing closes that gap. The signal is the TEE concept, attestation, and the AI workloads that actually need it.

Privacy in ML is a toolbox, not a single switch, and each tool defends a different threat. The signal is mapping DP, federated learning, confidential computing, encryption, and minimization to what they actually protect, and knowing they compose.