Public surveillance systems are necessary for security yet pose significant privacy risks due to unauthorized facial recognition. Traditional privacy-preserving methods, such as face blurring, are not practical in preventing the leakage of identity. This paper presents a novel framework that will enhance privacy preservation in surveillance through the integration of deep learning-based facial feature extraction, adversarial encryption, and homomorphic encryption CKKS. The approach first detects faces using YOLOv8, followed by extracting features with ResNet-18 that provides a 512-dimensional facial embedding. This work conceptually integrates CKKS homomorphic encryption to illustrate where secure facial feature processing could be applied in future systems. In the current prototype, actual encryption is not performed; instead, unencrypted 512-dimensional facial embeddings are visually projected onto the face regions to simulate anonymization. This provides privacy protection without exposing raw pixel data or identity-related visual information. Experimental results on real-world datasets validate the robustness of the encrypted representation against facial recognition models while maintaining video integrity. The proposed framework strikes a balance between security and privacy, thus providing a real-time privacy-preserving solution for surveillance applications. Optimization of computational efficiency and extending the approach to multimodal biometric systems are some of the future research directions.