<![CDATA[The exponential growth of video surveillance, live streaming platforms, and AI-driven analytics has created unprecedented threats to visual privacy. Traditional de-identification methods (pixelation, blurring) fail to balance privacy protection with contextual utility in dynamic environments. This systematic review of 30+ peer-reviewed studies uses a taxonomical framework to classify machine learning-based privacy preservation techniques into three domains: intervention methods (sensor saturation, broadcasting commands), obfuscation strategies (encryption, morphing, adaptive blurring), and secure processing pipelines. We test convolutional neural networks (CNNs), YOLO-based object detection systems, and hybrid approaches including GAN-driven synthetic data substitution using public datasets (MARS, DukeMTMC, Market-1501). CNN-YOLO hybrid architectures provide 30+ FPS real-time performance with 92-98% detection accuracy, while GAN-based anonymization preserves visual usefulness better than traditional approaches. Dataset scalability, illumination variability handling (accuracy drops 15-23% in low-light settings), occlusion resilience, and adversarial attack vulnerability remain key shortcomings. Although promising, lightweight encryption approaches for edge devices cost 12-18% processing speed and lack defined privacy-utility trade-off measures. Implications: This work unifies computer vision, cryptography, and privacy engineering into a single taxonomy, showing that context-aware frameworks need multi-level security designs to manage varied threat scenarios. Our findings help practitioners choose strategies depending on deployment restrictions (computational resources, latency, privacy regulations), yet 67% of reviewed methods lack real-world validation outside controlled datasets.This review uniquely synthesizes intervention, obfuscation, and secure processing research to provide uniform standards, context-adaptive privacy frameworks, and adversarially-robust de-identification systems. Five key research directions—federated learning for distributed privacy, attention-mechanism-enhanced detection under occlusion, and explainable AI for privacy-utility optimization—will shape the next generation of ethical, scalable visual privacy solutions in pervasive video analytics.]]>
