Secure process for validating machine learning models using homomorphic encryption techniques

A method for secure validation of machine learning models and parallel validation data using homomorphic encryption, comprising:

• providing, by a provider, a machine learning model and providing, by a user, validation data;
• encrypting, by the provider, the machine learning model;
• sending, by the provider, a public encryption parameter to the user;
• selecting, by the user and provider, a unifying encoding method;
• encrypting, by the user, the validation data;
• sending, by the user, the encrypted validation data;
• processing, the encrypted validation data with the encrypted machine learning model;
• providing encrypted results of said processing to the provider and the user;
• decrypting the results and evaluating whether the performance of the machine learning model is satisfactory with the given valuation data of the user;
• and
• wherein a neural network watermarking is used to trace the machine learning model upon a determination that a redistribution of the provided machine learning model is not to occur.

A system for secure validation of machine learning models and parallel validation data using homomorphic encryption, the system comprising a provider and a user, the provider including a computer device having a processor connected to a non-transitory computer readable medium, the user including a computer device having a processor connected to a non-transitory medium;

• the user being communicatively connectable to the provider, wherein the system is configured such that
• a machine learning model is providable via the provider and validation data is providable via the user;
• the provider configured to encrypt the machine learning model and send a public encryption parameter to the user;
• the user and the provider configured to select a unifying encoding method;
• the user configured to encrypt the validation data and send the validation data so that the validation data is processable by the encrypted machine learning model to output encryption results that are providable to the user and the provider, the user and the provider being configured to decrypt the encryption results and evaluate whether the machine learning model performed satisfactorily with the valuation data, and in response to the machine learning model performing unsatisfactorily with the valuation data and does not meet requirements for accuracy and/or efficiency, the encrypted machine learning model processing of the validation data is repeatable with another unifying coding method.