Multi-Trigger-Key: Toward Multi-Task Privacy Preserving in Deep Learning

Abstract

Deep learning-based Multi-Task Classification (MTC) is widely used in applications such as facial attributes and healthcare, which require robust privacy guarantees. In this study, we propose a novel Multi-Trigger-Key (MTK) framework to fulfill the privacy-preservation objective of protecting sensitive information throughout the entire workflow of MTC. We provide two real world examples that demonstrates how MTK can be implemented in the context of healthcare and financial tasks. Each secured task in the multi-task dataset is linked to a specially crafted trigger-key, processed by a data distributor, a secret key distributor, an assembler, and a model optimizer/keeper in the MTK system. If a user is authorized to access certain data, the insertion of trigger keys will reveal the accurate information. Furthermore, the learning process is structured to allow the four MTK agents to collaboratively distribute privacy protection. To address the information leakage problem caused by correlations among different classes, MTK training also includes a tuning parameter, which is used to balance the protective efficacy and model performance. Theoretical assurances and experimental results demonstrate that privacy protection is effective without significantly compromising model performance.

Keywords

• Deep learning
• neural network
• multi-task
• privacy
• trigger