What is privacy-preserving artificial intelligence inference?
Definition
Privacy-preserving artificial intelligence inference limits what infrastructure operators and other participants can learn about a request. Techniques can include local processing, trusted execution environments, encrypted transport, confidential computing, data minimization, or cryptographic methods designed for computation on protected data.
A privacy claim depends on the complete threat model, not only the model runtime. Operating-system privileges, logs, memory access, network metadata, updates, and control services may expose information. Independent review and measured evidence are important when remote workloads run on hardware owned by strangers.
Acronyms and aliases
privacy-preserving AI inference variantprivate model inference variant
Related terms
Frequently asked questions
What data should privacy-preserving inference protect?
It should protect prompts, uploaded context, generated outputs, credentials, model state where required, and metadata that could reveal sensitive user activity.
Does a hardened process prove that remote inference is private?
No. Privacy depends on the operating system, hardware, logging, updates, network, control services, and whether the stated protections have been independently verified.
