在基於雲的人工智能即服務 (AIaaS) 中，在處理立即理解人們的行為/交互時，魯棒的人類行為/交互識別 (HAR/HIR) 至關重要，特別是在監控系統、遠程互聯網等普遍系統中。醫療和人機交互。雖然深度學習(DL) 方法在 HAR/HIR 中得到了重視，但隱私問題也越來越重要，特別是當視頻數據在公共場所捕獲並直接由 DL 模型使用而無需額外保護時。
除了隱私之外，安全性、快速推理和低延遲對於 AIaaS 的成功採用也起著至關重要的作用。為了應對這些挑戰，本文討論了面向普及 HAR/HIR的隱私感知綜合框架。首先，研究了用於 HAR/HIR 任務的基於骨架的模型。其次，安全且保護隱私的 HIR 框架應用於普遍的醫療保健監控任務，稱為 STGCN-PAM-EFCC。在 STGCN-PAM-EFCC 中，使用人體姿勢估計 (PoseNet) 將人體動作視頻數據模糊為骨架數據。然後，添加了具有輕量級加密模式的安全層。儘管兩個 DL 協同工作會增加額外成本，但可以在保持識別性能、低延遲和安全普遍的 HAR/HIR 的同時保護數據隱私。
後續部分提供了詳細的描述、實驗結果和分析。

Robust Human action/interaction recognition (HAR/HIR) is crucial when
dealing with immediately understanding the actions/interactions of people in a Cloud-Based Artificial Intelligence as a Service (AIaaS), specifically in pervasive systems such as surveillance systems, remote internet
of medical things, and human-robot interaction. While Deep Learning
(DL)approaches have gained prominence in HAR/HIR, privacy concerns
are increasingly significant, especially when video data is captured in public places and directly utilized by DL models without additional protection.
In addition to privacy, security, fast inference, and low latency play a vital
role in the success of the AIaaS adoption. Addressing these challenges, this
dissertation discusses the comprehensive framework for privacy-aware toward pervasive HAR/HIR. First, a skeleton-based model was studied for
HAR/HIR tasks. Second, a secure and privacy-preserving HIR framework
is applied to pervasive healthcare monitoring tasks called STGCN-PAMEFCC. In STGCN-PAM-EFCC, human action video data were obscured
to skeleton data using Human Pose Estimation (PoseNet). Then, a security layer was added with a lightweight encryption schema. Despite adding
extra cost with two DLs working collaboratively, preserving data privacy
while maintaining the recognition performance, low latency, and secure
pervasive HAR/HIR can be achieved. Detailed descriptions, experimental
results, and analysis are provided in subsequent sections.

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