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Facial phenotyping has recently been successfully exploited for medicaldiagnosis as a novel way to diagnose a range of diseases, where facialbiometrics has been revealed to have rich links to underlying genetic ormedical causes. In this paper, taking Parkinson's Diseases (PD) as a casestudy, we proposed an Artificial-Intelligence-of-Things (AIoT) edge-orientedprivacy-preserving facial diagnosis framework to analyze the treatment of DeepBrain Stimulation (DBS) on PD patients. In the proposed framework, a newedge-based information theoretically secure framework is proposed to implementprivate deep facial diagnosis as a service over a privacy-preservingAIoT-oriented multi-party communication scheme, where partial homomorphicencryption (PHE) is leveraged to enable privacy-preserving deep facialdiagnosis directly on encrypted facial patterns. In our experiments with acollected facial dataset from PD patients, for the first time, we demonstratedthat facial patterns could be used to valuate the improvement of PD patientsundergoing DBS treatment. We further implemented a privacy-preserving deepfacial diagnosis framework that can achieve the same accuracy as thenon-encrypted one, showing the potential of our privacy-preserving facialdiagnosis as an trustworthy edge service for grading the severity of PD inpatients.

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