Public surveillance systems are known to serve extremely important purposes in many cities around the world. However, they create serious doubts regarding privacy concerns. This paper resolves that by using advanced face detection and recognition algorithms to protect individual privacy. Our approach relies on YOLOv8(You Only Look Once version 8) is a state-of-the-art object detection model that improves previous iterations of the YOLO series for face detection and has been trained on both face detection and WIDERFACE datasets (The WiderFace dataset is a large-scale benchmark designed for face detection tasks). In order to have only the face of the individual under tracking, we employ a face matching algorithm for its accurate recognition. Other faces in the surveillance video are blurred by the average filter to preserve the individual privacy. Thus, public security alongside the need for protection of personal privacy is a delicate balancing act that offers a robust solution for privacy in systems of public surveillance. Our experimental results demonstrated that our approach makes privacy possible without compromising the behavior of the system as far as public space monitoring and security are concerned.