Secure machine learning analytics using homomorphic encryption

A system for performing a secure machine learning analysis in an environment using homomorphic encryption, the system comprising:

• at least one processor in an environment;
• and
• a memory communicatively coupled with the at least one processor, the memory storing instructions, which when executed by the at least processor perform a method comprising:
• receiving, from a client, by at least one server in the environment, an encrypted machine learning data structure formed by using a homomorphic encryption scheme to encrypt a machine learning data structure that has been generated by training a decision tree machine learning model that contains the machine learning data structure, the training performed in a trusted environment,
• the machine learning data structure including at least one feature vector having a feature depth that is greater than a decision depth of the decision tree machine learning model,
• the machine learning data structure based on the trained decision tree machine learning model;
• extracting, by the at least one server, a previously unseen instance of data;
• evaluating, by the at least one server, the encrypted machine learning data structure over the previously unseen instance of data using the decision tree machine learning model containing the encrypted machine learning data structure to generate at least one encrypted result about the previously unseen instance of data;
• and
• sending, from the at least one server, the at least one encrypted result to the client, the at least one encrypted result configured to be decrypted at the client using the homomorphic encryption scheme.

A non-transitory computer-readable storage medium having embodied thereon instructions, which when executed by at least one processor, perform steps of a method, the method comprising:

• receiving, from a client, by at least one server in an environment, an encrypted machine learning data structure formed by using a homomorphic encryption scheme to encrypt a machine learning data structure that has been generated by training a machine learning model that contains the machine learning data structure, the training performed in a trusted environment, the trained machine learning model being a decision tree that has a decision depth, the encrypted machine learning data structure including at least one feature vector having a feature depth that is greater than the decision depth of the trained machine learning model, the machine learning data structure based on the trained machine learning model;
• extracting, by the at least one server, a previously unseen instance of data;
• evaluating, by the at least one server, the encrypted machine learning data structure over the previously unseen instance of data using the machine learning model containing the encrypted machine learning data structure to generate at least one encrypted result about the previously unseen instance of data;
• and
• sending, from the at least one server, the at least one encrypted result to the client, the at least one encrypted result configured to be decrypted at the client using the homomorphic encryption scheme.

A method for performing a secure machine learning analysis using homomorphic encryption, the method comprising:

• receiving, from a client, by at least one server in an environment, an encrypted machine learning data structure formed by using a homomorphic encryption scheme to encrypt a machine learning data structure that has been generated by training a neural network machine learning model that contains the machine learning data structure, the training performed in a trusted environment,
• the neural network machine learning model including a feature vector having a decision depth and a feature vector having a feature depth that is greater than the decision depth,
• the machine learning data structure based on the trained neural network machine learning model;
• extracting, by the at least one server, a previously unseen instance of data;
• evaluating, by the at least one server, the encrypted at least one machine learning data structure over the previously unseen instance of data using the neural network machine learning model containing the encrypted machine learning data structure of the neural network machine learning model to generate at least one encrypted result about the previously unseen instance of data;
• and
• sending, from the at least one server, the at least one encrypted result to the client, the at least one encrypted result configured to be decrypted at the client using the homomorphic encryption scheme.