Privacy-preserving Deep Learning for Autism Spectrum Disorder Classification

Guangmao Gao, Hanlin Zhang, Jie Lin, Hansong Xu, Fanyu Kong, Leyun Yu

Abstract: Autism Spectrum Disorder (ASD) encompasses a range of complex neurodevelopmental conditions typically identified in early childhood. ASD is characterized by challenges in social interaction, communication, and by repetitive behaviors with restricted interests. The variability in symptoms’ severity and expression among individuals presents significant diagnostic challenges to physicians. Advancements in computer technology have led various fields to adopt deep learning for constructing classification models. However, given the private nature of patient data, its leakage could have grave consequences. To mitigate this risk, we employ secure multiparty computing techniques and introduce a deep learning framework that ensures data interoperability without compromising privacy. Our framework facilitates deep learning training and inference via a lightweight, replicated secret-sharing technique. Experimentally, the scheme has been proven to exhibit high security, accuracy, and efficiency.