With the increasing adoption of Internet of Things (IoT) devices in smart homes, wearables, and public environments, privacy concerns have emerged as a significant challenge. Traditional privacy mechanisms often rely on static policies or machine learning models that require high computational power, making them unsuitable for resource-constrained IoT devices. This paper presents a novel real-time, context-aware privacy adaptation approach that dynamically adjusts privacy settings based on multiple contextual factors, including user presence, location, and network security status. Unlike existing solutions that either focus solely on access control or rely on computationally intensive models, our rule-based approach provides a lightweight, fast, and developer-friendly solution. Our results demonstrate improved privacy protection while ensuring computational efficiency, making it well-suited for IoT applications in smart homes, wearables, and other resource-constrained environments.