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

Vinayak Raja ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 4, issue 1, 152-162

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.

Keywords: privacy-preserving machine learning; collaborative data sharing; confidentiality; sensitive information; machine learning process; model training (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:das:njaigs:v:4:y:2024:i:1:p:152-162:id:129

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