Training Decision Trees Securely and Efficiently with Data from Multiple Parties

What is it about?

Our study introduces "Ents," a system that allows multiple groups to work together to train decision tree models while keeping their private data secure. Decision trees are widely used in fields like healthcare and finance because they are easy to interpret. However, current methods for securely training these models with data from multiple parties are slow and require a lot of communication, making them impractical for real-world use. To address this, we developed a more efficient approach that reduces communication and speeds up the process. This makes it possible to use decision trees securely in practical situations, such as when hospitals collaborate on medical diagnoses without sharing raw patient data.

Featured Image

Photo by niko photos on Unsplash

Photo by niko photos on Unsplash

Why is it important?

Our work addresses a critical challenge in privacy-preserving machine learning: securely training decision tree models across multiple parties with minimal communication overhead. This is timely because the demand for collaborative analysis of private data is growing rapidly, especially in fields like healthcare and finance, where regulations such as GDPR require strict privacy protections. What makes our approach unique is its ability to drastically reduce communication costs while maintaining strong data privacy. By making secure training faster and more efficient, our work paves the way for practical applications, enabling organizations to unlock the value of shared data without compromising confidentiality.

Perspectives