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.
翻译:面部生物鉴别法被揭示为与基因或医学原因有丰富联系的私人深层面部诊断服务。 本文将帕金森病(PD)作为案例研究,建议采用人工-智能面部诊断框架来直接根据加密面部模式进行深层面部诊断。 我们首次对从PD病人收集的面部数据集进行了实验,我们证明,面部模式可用于评估接受DBS治疗的PD病人的改善情况。 我们还进一步采用了一个隐私深层面部诊断框架,以作为维护隐私的AIOT型多党沟通方案的一项服务,以部分同质加密(PHE)为工具,以便直接对加密面部模式进行隐私保存深刻的面部诊断。 我们首次演示了从PD病人收集的面部数据集的实验,可以用来评估接受DBS治疗的PD病人的改善情况。 我们还进一步落实了一个隐私深层面部诊断框架,以维护患者的面部诊断能力为一种不可靠的水平。