Systems and methods for secure multi-model training within a zero-trust environment

Abstract

Systems and methods for federated localized feedback and performance tracking of an algorithm is provided. An encrypted algorithm and data are provided to a sequestered computing node. The algorithm is decrypted and processes the protected information to generate inferences from dataframes which are provide to an inference interaction server, which performs feedback processing on the inference/dataframe pairs. Further, a computerized method of secure model generation in a sequestered computing node is provided, using automated multi-model training, leaderboard generation and then optimization. The top model is then selected and security processing on the selected model may be performed. Also, systems and methods are provided for the mapping of data input features to a data profile to prevent data exfiltration. Data consumed is broken out by features, and the features are mapped to either sensitive or non-sensitive classifications. The sensitive information may be obfuscated, while the non-sensitive information may be maintained.