Securing Machine Learning Models with Homomorphic Encryption: A Practical Guide

In the age of data-driven decision-making, ensuring the security and privacy of sensitive information is paramount.Homomorphic encryption emerges as a robust solution, allowing the deployment of machine learning models while preserving the confidentiality of sensitive data.

In this article, we explore a real-time example of securing a machine learning model with homomorphic encryption, discussing the outputs and providing insights on measuring feasibility.

In any ML model deployment, when there is a concern about security risks involved, we think about solving the issue and come up with various solutions for securing our model for deployment such as Role based Authentication, validating the inputs of the model, or secure DevOps practices involving the infrastructure and so on.

I came across similar solution that securing can be done by encrypting the data to prevent any leaks or ensure privacy of data. Such a technique is Homomorphic Encryption

Understand Homomorphic Encryption

It is a powerful technique for securing machine learning (ML) models during deployment. Also, It allows computations to be performed on encrypted data without decrypting it.