Confidentiality Solutions for Distributed Machine Learning Models

In modern collaborative environments, the secure transfer of machine learning models between distributed systems is essential for ensuring consistent decision-making and facilitating seamless cooperation. This paper presents a novel approach for securely transferring a machine learning model, selected from various algorithms such as linear regression, K-nearest neighbors (KNN), decision tree, and random forest, across distributed systems. Our method involves encrypting the selected model using the Advanced Encryption Standard (AES) algorithm, while the AES key is encrypted using the ElGamal encryption scheme. This double-layered encryption ensures both the confidentiality of the model and the security of the encryption key during transmission. At the receiver's end, the encrypted model and key are decrypted, enabling predictions to be made using the recovered model. Through this process, the integrity and confidentiality of the transferred model are preserved, thereby mitigating the risk of unauthorized access or tampering. We discuss the implementation details of our approach, including the tools and technologies utilized, as well as the performance evaluation of the encrypted model transfer process. Our findings demonstrate the effectiveness of the proposed method in securely transferring machine learning models between distributed systems, thereby fostering collaboration and knowledge sharing while maintaining data privacy and security. This research contributes to the advancement of secure model transfer techniques in distributed environments, with implications for various domains requiring collaborative machine learning applications.