Secure Outsourcing of Machine Learning Models to Untrusted Cloud Servers

Abstract

With the proliferation of cloud computing, outsourcing machine learning (ML) models to untrusted cloud servers has become prevalent but raises security concerns. This paper proposes a framework for securely outsourcing ML models to mitigate risks associated with data privacy and model integrity. Our approach leverages cryptographic techniques such as homomorphic encryption and secure multiparty computation (SMC) to ensure that sensitive data and model parameters remain encrypted during computation on the cloud server. We evaluate the framework's performance in terms of computation overhead and security guarantees, demonstrating its effectiveness in protecting against unauthorized access and tampering. Through experimental validation, we illustrate the feasibility and efficiency of our proposed solution, highlighting its potential applications in various domains requiring secure and scalable ML model outsourcing.