A 3D Scene Graph (3DSG) is a hierarchical 3D representation of a physical environment. Hydra is a promising real-time spatial perception framework for 3DSG developed by MIT Spark Lab. It uses sensor and camera data from an agent to construct a graph of the environment with five layers and continuously updates it. Albeit its utility and efficiency in generating and updating real-time 3DSG using visual-inertial sensors, Hydra and many other 3DSG frameworks ignore violating to identify private sensitive objects by segmenting, identifying, and reconstructing everything in data. Therefore, in this paper, we enhance Hydra to preserve sensitive data and increase the privacy-awareness of the framework. Accordingly, we propose a Privacy-aware Hydra (Pa-Hydra) framework, which integrates a state-of-the-art Object Detection (OD) algorithm that utilizes a proposed Filter Algorithm to cover sensitive objects with black boxes and prevent them from being constructed by Hydra. We implemented the framework with two popular OD algorithms with two different pre-trained models: You Only Look Once version 9 (YOLOv9) and Real-time Detection Transformer (RTDETR). We evaluated the algorithms using the COCO2017 validation dataset and observed the efficiency of the proposed framework on the uHuman2 (uH2) dataset.