The medical field, known for its strict regulations concerning patient confidentiality, has long sought innovative solutions to leverage big data without compromising privacy. This article focuses on the role of isolation transformers in addressing these challenges, showcasing how they enable secure and efficient analysis of sensitive health data.

In the rapidly evolving landscape of data science and machine learning, isolation transformers have emerged as a pivotal technology, particularly in scenarios requiring robust data privacy. This paper delves into the innovative design principles of isolation transformers and their significant contributions to distributed systems. We will explore how these models, originally proposed by researchers in the field of deep learning, facilitate secure data processing without the need for direct data sharing or exposure.

Isolation transformers are used to provide electrical isolation between two circuits. They have a primary winding and a secondary winding that are electrically separated, which means there is no direct electrical connection between the two windings.