Secure Transfer Learning Across Untrusted Domains

Abstract

In the evolving landscape of machine learning, transfer learning has emerged as a powerful technique to enhance model performance by leveraging knowledge from related domains. However, transferring knowledge across untrusted domains introduces significant security and privacy challenges. This paper presents a comprehensive framework for secure transfer learning, designed to address these challenges. Our approach incorporates robust encryption mechanisms, differential privacy, and adversarial training to safeguard sensitive data and model integrity throughout the transfer process. We demonstrate the efficacy of our framework through extensive experiments across various benchmark datasets, highlighting its ability to maintain high accuracy while ensuring security against potential threats. Our findings underscore the importance of integrating security measures in transfer learning pipelines, paving the way for broader adoption in applications where data privacy and trust are paramount.