Private Facial Diagnosis as an Edge Service for Parkinson's DBS Treatment Valuation

TL;DR

This paper introduces a privacy-preserving AIoT framework for facial diagnosis of Parkinson's Disease, enabling secure, accurate assessment of treatment progress directly on encrypted facial data at the edge.

Contribution

It presents a novel edge-based, information-theoretically secure facial diagnosis framework using partial homomorphic encryption for Parkinson's Disease assessment.

Findings

Facial patterns can evaluate PD treatment improvement.

The framework achieves accuracy comparable to non-encrypted diagnosis.

Demonstrates feasibility of privacy-preserving facial diagnosis as an edge service.

Abstract

Facial phenotyping has recently been successfully exploited for medical diagnosis as a novel way to diagnose a range of diseases, where facial biometrics has been revealed to have rich links to underlying genetic or medical causes. In this paper, taking Parkinson's Diseases (PD) as a case study, we proposed an Artificial-Intelligence-of-Things (AIoT) edge-oriented privacy-preserving facial diagnosis framework to analyze the treatment of Deep Brain Stimulation (DBS) on PD patients. In the proposed framework, a new edge-based information theoretically secure framework is proposed to implement private deep facial diagnosis as a service over a privacy-preserving AIoT-oriented multi-party communication scheme, where partial homomorphic encryption (PHE) is leveraged to enable privacy-preserving deep facial diagnosis directly on encrypted facial patterns. In our experiments with a collected facial dataset from PD patients, for the first time, we demonstrated that facial patterns could be used to valuate the improvement of PD patients undergoing DBS treatment. We further implemented a privacy-preserving deep facial diagnosis framework that can achieve the same accuracy as the non-encrypted one, showing the potential of our privacy-preserving facial diagnosis as an trustworthy edge service for grading the severity of PD in patients.