Fostering Privacy in Collaborative Data Sharing via Auto-encoder Latent Space Embedding

Abstract

Securing privacy in machine learning via collaborative data sharing is essential for organizations seeking to harness collective data while upholding confidentiality. This becomes especially vital when protecting sensitive information across the entire machine learning pipeline, from model training to inference. This paper presents an innovative framework utilizing Representation Learning via autoencoders to generate privacy-preserving embedded data. As a result, organizations can distribute these representations, enhancing the performance of machine learning models in situations where multiple data sources converge for a unified predictive task downstream.