Kernels

Prepares encrypted raw data for a model — scaling, binning, encoding and aggregation — so the preparation step stops being the place where privacy is quietly lost.

Decides whether a transaction is fraudulent without the classifier ever seeing it. The bank keeps its model, the customer keeps their transactions, and the verdict still gets made.

Classifies an image that stays encrypted from the camera to the answer. The server learns neither the picture nor its label, which is the whole proposition of private inference.

Produces recommendations from a purchase history that stays encrypted. The store learns what to show without learning what was bought.

Predicts a number from encrypted features. Valuation, scoring and forecasting are all this, and doing it under encryption lets a model be offered as a service without either side surrendering its data.

Counts encrypted ballots and reports the outcome without any individual vote becoming visible. The oldest use of homomorphic encryption and still the clearest: adding up numbers nobody is allowed to read.

Assigns a label to text that stays encrypted. Moderation, triage and sentiment scoring performed by a service that never reads the message it is judging.