Encryption and Real Time Decryption for protecting Machine Learning models in Android Applications

TL;DR

This paper presents a secure, efficient, and easy-to-implement encryption algorithm for protecting machine learning models in Android apps, ensuring real-time decryption without UI interruption.

Contribution

It introduces a novel encryption and real-time decryption method using AES-256 that enhances security and reduces implementation effort for mobile ML models.

Findings

The algorithm is fast and scalable for large models.

It provides high security with AES-256 encryption.

Implementation effort is minimal compared to existing methods.

Abstract

With the Increasing use of Machine Learning in Android applications, more research and efforts are being put into developing better-performing machine learning algorithms with a vast amount of data. Along with machine learning for mobile phones, the threat of extraction of trained machine learning models from application packages (APK) through reverse engineering exists. Currently, there are ways to protect models in mobile applications such as name obfuscation, cloud deployment, last layer isolation. Still, they offer less security, and their implementation requires more effort. This paper gives an algorithm to protect trained machine learning models inside android applications with high security and low efforts to implement it. The algorithm ensures security by encrypting the model and real-time decrypting it with 256-bit Advanced Encryption Standard (AES) inside the running application. It works efficiently with big model files without interrupting the User interface (UI) Thread. As compared to other methods, it is fast, more secure, and involves fewer efforts. This algorithm provides the developers and researchers a way to secure their actions and making the results available to all without any concern.