A financial services company deploys a machine learning model via a public prediction API that returns detailed confidence scores for each classification. Security testing reveals that an external researcher was able to reconstruct attributes of individuals in the training set and determine whether specific customer records were used to train the model. Which combination of controls best mitigates these model inversion and membership inference attacks while preserving the API's usefulness?